Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Abstract

Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

1. Introduction

E-commerce has grown significantly in recent years, both in terms of the number of users and the number of commercial websites. eMarketer [1] estimated that the growth of e-commerce sales in 2020 over the previous year would be 27.6%, for a total of USD 4.28 trillion. Statista [2] projected that e-retail sales would increase to USD 5.4 trillion by 2022. Despite the fact that the past few years have been difficult for retail, the COVID-19 pandemic is significantly affecting e-commerce due in major part to a shift in consumer behavior [3]. Merchants of non-essential items, such as clothing and footwear, are experiencing a decline in sales, while retailers of vital goods, such as food, consumables, and healthcare, have seen an increase in online shopping [4]. The COVID-19 pandemic has fundamentally altered international trends and compelled quick reforms in several industries. Despite the challenges the industry is facing, Statista [5] reports that the fashion industry is the largest Business-to-Customer (B2C) e-commerce market segment. By the end of 2025, the industry is projected to have a total market value of USD 1003.5 billion.

Artificial intelligence (AI) has altered numerous industries over the past few decades, including healthcare [6,7], manufacturing [8,9], transportation [10,11] and retail [12,13], The application of AI is also on the rise in e-commerce strategies [14,15,16,17]. Many merchants are already utilizing artificial intelligence (AI) technologies as a driving force for the development of e-commerce in a competitive environment where consumers are becoming more demanding. As an example, consider how e-commerce behemoths like Amazon, Alibaba, and eBay invest in research and development to integrate visual recognition techniques, develop algorithms to meet user content preferences, or adjust pricing based on in-the-moment comparisons of rival products. E-commerce makes more information available to both consumers and rival businesses. E-commerce retailers are compelled to take on new AI techniques due to fierce online market rivalry.

Within this context, the goals of this investigation can be listed as follows: First, to analyze the current trend of AI in the fashion e-commerce sector and the future of AI technology. Second, to understand how the sector’s use of AI technology enhances firm profitability. Third, to identify knowledge gaps that might be investigated by future scholars. In order to accomplish these objectives, the following research approach is presented.

2. Research Approach

The research questions (RQs) plus the methodology to respond to them and, consequently, deal correctly with the previously detailed objectives are presented herein.

The following research issues are addressed in this study:

• RQ1: What are the uses of AI technology in the e-commerce world of fashion?

• RQ2: How can the fashion sector use AI to its fullest potential in order to increase customer satisfaction and financial success?

• RQ3: What are the hot topics and upcoming research directions in the field of AI for the e-commerce industry of fashion?

A narrative literature review (NLR) method was employed in this research as, compared to others such as the ones mentioned in Ref. [18], the (a) systematic, (b) scoping, (c) argumentative, (d) integrative, or (e) theoretical literature reviews, the NLR is better oriented to identify gaps in the existing knowledge base [19]. The narrative literature review methodology employed in this evaluation follows the suggestions of Green, Johnson, and Adams [20] and Ferrari [21] for the narrative overview variant. These narrative overviews are recognized as great, up-to-date papers [21]. Several research studies in the literature reviews of information systems, technological applications, and management science have effectively used this methodology [22,23], and that is the reason this methodology has been chosen. Thus, the main steps of a narrative document review approach are as follows:

• Determine the research questions;

• Develop inclusion and exclusion criteria;

• Conduct a thorough search of relevant databases and other sources;

• Review and select studies based on inclusion and exclusion criteria;

• Extract data from selected studies, and analyze and synthesize data extracted from studies.

It is important to note that the methodology may vary slightly depending on the specific research question or topic and the type of research being conducted. In our case, we have incorporated a sixth step, which is:

• 6.. Validation of results.

Thus, this manuscript’s contribution is an analysis of the current trend of AI in the fashion e-commerce sector, the future of AI technology, and how the sector’s use of AI technology enhances firm profitability, by applying an NLR process to identify and analyze research literature in the field of AI for the e-commerce industry of fashion. The NLR method is commonly used in research studies to provide an unbiased, comprehensive, and rigorous analysis of the existing literature. For that reason, the authors have not discussed the pros and cons of each of the techniques, in order to reduce the possibility of biasing the study, as the reasons for using one or another technique can be extremely varied.

Based on the research initiative that uses the NLR as a core, Figure 1 explains the flow this work has followed to respond to the three RQs.

As indicated in Figure 1, this research starts from the formulation of the three research questions shown at the beginning of Section 2. Afterwards, it defines the scope (inclusion and exclusion criteria concerning sources and keywords) and performs the search based on it. Lastly, as a third step, analysis and synthesis of data extracted from studies to respond to RQ1, RQ2, and RQ3 are performed, resulting in responses to each of the questions as follows:

• RQ1: What are the uses of AI technology in the e-commerce world of fashion? Response: Summary table of purposes with references.

• RQ2: How can the fashion sector use AI to its fullest potential in order to increase customer satisfaction and financial success? Response: Table of methods and techniques used with references, table of reference datasets used to train and test the tools (and gain confidence on them), and table of datasets and purposes per dataset.

• RQ3: What are the hot topics and upcoming research directions in the field of AI for the e-commerce industry of fashion? Response: Further research section.

Thus, the rest of the paper is structured as follows. The criteria for inclusion and exclusion of items are described in Section 3. Section 4 discusses the uses of the techniques in the scope of the article. Section 5 addresses the second research question and describes the AI approaches used to increase consumer satisfaction along with the main fashion databases used in the articles included in the review. The third research question and the validation of the results are addressed in Section 6, Section 7 and Section 8, looking at forecasting future trends, as they present the research gaps that have been found.

Analysis and synthesis of data extracted from studies to respond to RQ1, RQ2, and RQ3 are as follows:

• RQ1: What are the uses of AI technology in the e-commerce world of fashion? Response: Summary table of purposes with references (Section 3).

• RQ2: How can the fashion sector use AI to its fullest potential in order to increase customer satisfaction and financial success? Response: Table of methods and techniques used with references, table of reference datasets used to train and test the tools (and gain confidence about them), and table of datasets and purposes per dataset (Section 4).

• RQ3: What are the hot topics and upcoming research directions in the field of AI for the e-commerce industry of fashion? Response: Hot topics and conclusions sections (Section 5 and Section 6, respectively).

3. Article Inclusion and Exclusion Criteria and Overall Results

Article selection was made using scientific databases, specifically Web of Science and Scopus, two of the most important repositories for publications. To examine all relevant studies in the field and complete RQ1’s aim, no time restrictions were imposed during the search procedure. The search terms include 60 fashion e-commerce synonyms as well as AI synonyms. The final search terms utilized for the investigation are shown in Table 1.

A total of 392 references were discovered using these search terms, including 108 duplicates between the two sources; 124 were in Web of Science and 268 in Scopus.

Ultimately, 252 references were examined. Due to the ambiguity of the word fashion and the lack of association with the issue under study, 65 articles were discarded after reading them. Many of these articles refer to fashion trends in the context of AI used in e-commerce rather than specifically in the fashion business. For the literature review, 245 research articles were read. The distribution of documents over the years is shown in Figure 2, and it has been observed that the number of papers published in the previous three years has significantly increased.

4. RQ1: Uses of AI Technology in the e-Commerce World of Fashion

The information extraction process used to answer research questions RQ1, RQ2, and RQ3 is detailed in this section. Based on Ref. [24], this study divides artificial intelligence (AI) into three major categories: computer vision (CV), natural language processing (NLP), and other machine learning (ML) applications. The reasoning behind this division is that, when dealing with garment data, most authors process the images of the garments, their text description, or other data, with the first two being the more prevalent ones. This process has been carried out by taking into account recent research on AI in fashion e-commerce. Figure 3 shows the classification of the number of manuscripts by topic and subtopic in accordance with the study objectives. In the same figure, the number of articles linked to each topic is displayed in brackets.

It is important to note that certain research publications are featured in more than one classification section because they either use a variety of AI methodologies or provide answers to many research questions.

5. RQ2: How the Fashion Sector Can Use AI to Its Fullest Potential in Order to Increase Customer Satisfaction and Financial Success

In order to correctly apply the potential of AI to the B2C fashion retail sector, it is necessary to identify both where it can be utilized and how it can be applied. The wideness of the ‘how’ term involves approaching the question from different angles.

If the articles read are approached in a more specific manner, additional sorting information regarding the databases used by the studied articles can be offered, as well as the techniques they apply and the specific purpose they have. Thus, these sections respond to how the sector can use AI, identifying:

• Most of the common algorithms used in the sector (Section 5.1);

• The purposes pursued when using each of the datasets (Section 5.2);

• The databases used to train and test the algorithms used (Section 5.3); and

• Actual examples of the way AI can be applied to customer satisfaction and lucrativeness (Section 5.4).

We respond to the four bullets in the list with several tables and figures. These tables and or figures show the amount of times each database, technique, purpose, etc., appears in the literature review. In all cases, the specific purposes, techniques, or databases appearing just once have been merged onto a single ‘Others’ group, to make feasible a clean and clear visualization of the charts (see e.g., Table 2).

5.1. Families of Algorithms Used in the B2C Fashion Retail Sector

As can be seen in Table 3, a plethora of AI techniques are used to process garment information for classification. They have been grouped by traditional machine learning techniques and deep learning techniques. Multiple authors use different techniques to solve the problems efficiently, with all of them being suitable in the right situation. In the following subsections, some of the AI algorithms used in the articles in this review are described (Table 4).

5.1.1. Traditional Machine Learning Techniques

Several traditional (not deep learning) algorithms have been applied to this task. One of the simplest sorting algorithms used in machine learning is the KNN algorithm. This classification approach uses space analysis to analyze the k-nearest neighbors [25]. Two other commonly used algorithms for fashion article classification are decision trees and random forests [26]. The naive Bayes utilizes a series of simplification operations based on the Bayes theorem and is based on the theory’s streamlining processes. The notion is that the classifier may be used to categorize the number of independent variables if there are too many of them. It is useless to apply probability tables when the number of independent variables is too big, which leads to the reductions that simplify the sample and give it the moniker “naive Bayes” [27]. A hyperplane that separates an n-dimensional representation of the data into two distinct areas serves as the foundation for support vector machines (SVM) classifiers. The area between two classes, also known as spatial regions, that maximizes the margin m between them is referred to as the hyperplane. This margin is computed from the distance of the specimens that are closest to the hyperplane and is defined as the longest distance between the specimens of the classes [28]. K-means is a vector quantization technique that was first used in signal processing to divide n observations into k clusters, with each observation belonging to the cluster that has the closest mean (also known as the cluster centroid or cluster center), which serves as a prototype for the cluster [29]. Recommender systems employ a method called collaborative filtering. By gathering preferences or taste data from several users, collaborative filtering is a technique for automatically predicting (filtering) a user’s interests (collaborating) [30]. A probabilistic model called a Gaussian mixing model posits that all of the data points are produced by combining a limited number of Gaussian distributions with unknown parameters [31]. Finally, fuzzy logic is an approach to computing based on degrees of truth that imitates human reasoning rather than Boolean logic [32]. The DART algorithm is an iterative improvement on multiple additive regression trees (MART), which is more robust towards class imbalances [33]. The Kneser–Ney algorithm calculates the probability of a word following a particular context by computing the raw probability of the word following the context and subtracting a discounting amount [34]. Finally, DDS is a single-shot detector algorithm that predicts the boundary boxes and the classes directly from feature maps in one single pass [35].

5.1.2. Deep Learning Techniques

Deep learning, commonly referred to as deep structured learning, is one of several machine learning techniques built on representation learning and artificial neural networks. Unsupervised, semi-supervised, and supervised learning are all possible [36]. Multiple deep learning algorithms have been used for this task in the literature.

Convolutional neural networks (CNN, or ConvNet) are a form of artificial neural network (ANN) that is often used to assess visual pictures. CNNs, also known as shift-invariant or space-invariant artificial neural networks, are based on the shared-weight architecture of the convolution kernels or filters that slide along input features and create translation-equivariant outputs known as feature maps (SIANN). Contrary to common perception, most convolutional neural networks downsample the input, which prevents them from translating invariantly. They are used in financial time series, natural language processing, brain–computer interfaces, image and video analysis, segmentation, classification, recommender systems, medical image analysis, and image and video recognition [37]. Multiple neural network layers make a deep convolutional neural network (DCNN). Convolutional and pooling layers, two different kinds, are often alternated. From left to right in the network, each filter’s depth rises. Usually, the final level consists of one or more completely linked layers [38]. Multiple variations of CNNs have been used, such as application of hierarchical classification, which takes advantage of the hierarchical structure of categories by embedding CNNs into a category hierarchy [39], or graph convolutional neural networks, which represent similarities using a graph architecture and perform CNN, multiplying the input neurons by a set of weights [40].

Long short-term memory (LSTM) networks have been also employed in this task [41], both as standalones or as a combination with CNNs. Convolutional neural network long short-term memory is an architecture that uses CNN layers for feature extraction and LSTM to support sequence prediction, usually used for images and video inputs [42].

The natural language processing (NLP) tool Word2vec was introduced in 2013. With the help of a huge text corpus, the Word2vec technique employs a neural network model to learn word connections. Once trained, a model like this may identify terms that are similar or propose new words to complete a phrase. As the name suggests, Word2vec uses a specific set of integers called a vector to represent each unique word. Given vectors that are properly selected to capture the semantic and syntactic characteristics of words, the degree of semantic similarity between the words represented by those vectors can be determined using a straightforward mathematical function (cosine similarity) [43]. Another NLP processing model commonly used is BERT, or bidirectional encoder representations from transformers. Its innovation is applying the bidirectional training of transformers to language modeling [44].

A Siamese neural network, also known as a twin neural network, is a type of artificial neural network that uses the same weights to calculate equivalent output vectors from two distinct input vectors simultaneously. A precomputed version of one of the output vectors frequently serves as a benchmark for comparison with the other output vector. Despite being more precisely referred to as a distance function for locality-sensitive hashing, this is comparable to comparing fingerprints [45].

5.2. AI in B2C Retail and Application Areas of the Techniques

RQ1 is centered on the general AI trend in the fashion e-commerce sector. Figure 3’s summary of the literature review illustrates numerous subtopics employing various AI methods.

Numerous papers on computer vision (CV) focus on the early stage of exploitation and interpretation of the data offered by the photographs. These studies aim to enhance the annotation process’s object identification, segmentation, and classification algorithms [34,35,39,40,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120]. Another group of studies develops techniques for advising and helping customers by pairing products or examining the textures of garments [78,79,80,81,84,121,122,123,124,125,126,127,128]. Additionally, some authors [32,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152] are developing virtual reality technology to help customers with clothing fitting. Finally, CV can be used to spot fake logos and clothes [153,154]. Several techniques, including image processing and deep learning using convolutional neural network designs, are employed for all of these applications.

In order to transition the traditional buying experience to e-commerce, NLP is mostly utilized for fashion advice and recommendations [34,78,79,124,126,155,156,157,158,159,160,161,162,163,164,165,166,167,168]. Another significant area of study focuses on gathering data from consumers using sentiment analysis techniques, such as via reviews or social media [42,44,169,170,171,172,173,174,175,176,177,178,179,180]. Finally, studies on picture tagging using textual content analysis have been conducted [55,61,73,89,94,95,98,104,107,113,116,118,181,182,183,184].

A number of publications concentrate on using ML and data analytics together to enhance the administration of e-commerce businesses. Some authors have researched subjects like budget management, inventory issues, and sales and demand forecasting [177,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209]. Other authors [33,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236] aim to forecast product return or purchase intention by predicting consumer behavior. Several studies investigate personalized recommendation systems using ML approaches in relation to consumer behavior [230,231,237,238,239,240,241,242,243,244,245,246,247,248,249].

5.2.1. Fashion e-Commerce and Research Problems

This section provides an overview of the key research issues that have been raised in studies that have analyzed and applied AI approaches. The goal of each task determines how it is divided.

Garment Representation

A considerable number of articles concentrate on how clothing is represented in fashion accessories. It is the earliest stage of information exploration and interpretation, with the goal of being accurate so that it may be used in particular applications. The majority of fashion studies begin with a precise detection task. Classification, landmark recognition, and item retrieval are the three sub-tasks that make up fashion detection.

Data labeling. The quality of the data used directly affects the accuracy levels of the AI models. When data are unlabeled, the process of adding tags to the data enables machine learning models to learn and recognize those things. Machine-based annotation is a revolutionary method of labeling that allows data annotation to be done more quickly without sacrificing quality [183].

Clothes classification. An image is used as the input for the image classification job, which then generates the classification label using metrics like probability or precision. The number of distinct tags from the labeled data determines how many classes the model can categorize. Some classifications are distinct in the fashion industry, based on the problem to be approached, such as classifying apparel by category (such as t-shirt or frock) and by attribute (such as white or round neck). The convolutional neural network (CNN) is the most frequently used algorithm for classifying clothing [58]. Deep convolutional neural networks (DCNNs) [63], also known as these networks’ deep architectures, are used in a number of studies. Examples include VGG16 [57,62] and VGG19 [60].

Detection of landmarks. Fashion landmark identification anticipates where functional clothing keypoints like the neckline and cuff will be located. Because of the nature of the clothing, this is a more complicated challenge than pose estimation. A number of states, such as wrinkles or sags, can be found in clothing. In this review, two papers [35,66] that used DCNNs to improve this task are discussed.

Product retrieval. Finding comparable or identical objects in databases is the purpose of item retrieval tasks. The AI community has paid a lot of attention to fashion retrieval, mostly because it can be challenging to describe clothing in words and users typically find it simpler to search by image. The majority of research focuses on identifying identical or nearly identical clothing or even detecting plagiarism. The use of DCNN algorithms to resolve retrieval tasks has become popular due to deep learning advancements [54,86]. For this objective, generative adversarial networks (GAN) are also helpful [71,75]. In these networks, two neural networks compete to make predictions with greater accuracy. Finally, several studies employ support vector machines (SVMs) [64,87] during the classification phase of the item retrieval task.

Customer Satisfaction

There are two primary categories of customer-centered applications in this review. By using these programs, you can attempt to recreate the offline buying experience online.

Recommender systems. The goal of recommender systems is to provide users with useful suggestions. There are two main types of approaches for this task: content-based methods and collaborative filtering methods. Content-based approaches solely rely on past client information, such as purchases, browsing patterns, and search queries. The recommender system creates a model with the user’s characteristics using these data and looks for profiles with attributes that are comparable to the user’s. The interactions between users and objects constitute the foundation of collaborative approaches. These techniques do not need users to provide any information. Recommender systems connect users based on how they engage with the product.

There are additional recommender systems that are concerned with fashion and trends. These models typically use text and visual data to derive brand-new traits pertaining to style. The consumer is then given recommendations for related clothing, fashionable clothing, or clothing that goes well together.

The creation of fashion recommendation systems uses a range of AI models. To improve the prediction’s accuracy, researchers also change the algorithms and tweak the parameters. One of the most popular models is CNN, which consists of many layers, with the number of layers being tailored to the results of the recommendation system [81,123,128,163]. Numerous studies using deep neural networks (DNN) for recommendation tasks have been conducted [84,159].

Virtual fitting systems. Virtual fitting solutions are bridging one of the biggest gaps between e-commerce and physical stores: the inability to try on clothing. The ability to take body measurements and determine clothing size and fit are the two key topics discussed. Studies that employ 2D photographs and the ones that use 3D reconstructions are two distinct sorts of research cases.

The fit of virtual clothing is frequently evaluated using neural networks [144], including CNN [140] and GAN variations [141]. The employment of alternative algorithms, such as the naive Bayes (NB) algorithm for classification [149], the Viola–Jones (VJ) algorithm for body tracking [148], or the fuzzy neural network (FNN) [32], has also been the subject of some research.

Customer Behavior Forecasting

Many clients and items can be managed by businesses thanks to AI, which also makes it simple for them to understand every aspect of each product’s storage, distribution, and sale. Simple and real-time control and management are possible. Although many studies use historical data to build their forecasting models, social media is becoming a larger and more common source of information in recent years. Social media platforms provide insight into how consumers feel about things, making it possible to forecast future sales or alter products to better meet customer needs.

Sentiment analysis. An analysis of the mood on social media reveals how consumers feel online about a product or brand. It is an examination of feelings and opinions, not just a basic tally of mentions or comments. This kind of study enables businesses to understand their target market, spot trends, enhance customer experience, and spot crises in their earliest phases [174]. Many studies [42,169] use CNN for sentiment analysis, and occasionally [44] a final fine-tuning algorithm like BERT is constructed. The studies employ both SVM [172] and NB [179] for classifying opinions [179].

Business administration. The main focus of artificial intelligence in company management is on creating prediction models based on past data in order to improve management and uncover new customer-facing opportunities. Profit maximization, sales forecasting, shipping logistics, inventory management, and fraud detection are some of the key duties in this study problem. The majority of recent studies have an ML method focus [185,186,187,209].

Multimodal Systems

Combining various approaches is a very active topic of AI research. Since many of the works included in this review use a combination of CV and NLP techniques to achieve the goal, it was simple to see this effect [34,55,94,95,102,107,116,126,156,157]. When information from many modalities is included in a research problem, such as text–image, video–audio, or another combination, it is referred to as multimodal research. Compared to multimodal systems, a crossmodal system is a model that only receives data from a separate modality, such as when requesting an image response via text. A model produced by a multimodal system may have the same modality as the input or a different modality. In other words, multimodal systems can integrate several modalities together, such as text and visual. In particular, a crossmodal system, shown in Figure 4, is the process of using one modality to gain information in another modality.

Twenty-two publications on multimodal systems for item retrieval, classification, and apparel recommendation have been found in this study. The percentage of completed work for the various jobs may be seen in Figure 5.

5.2.2. Summary

The papers examined are summarized in this section. Table 5, Table 6 and Table 7, present the research’s techniques, objectives, and dataset.

5.3. Fashion Datasets

A quality dataset is essential for an AI-based model to produce the results that are expected. The fashion-related datasets that were found in the examined publications are summarized in this section. Table 8 presents the datasets and lists the name of each dataset, the publication year, the problem to be solved, some features, and the data source. It is significant to note that many authors choose to modify and produce datasets based on those shown below, or even to collect data from the internet, in order to address particular research issues.

5.4. AI Applied to Customer Satisfaction and Lucrativeness

The application of AI technologies to increase customer satisfaction and business profitability is documented in RQ2. Retailers are categorized as B2C since end users are their main clients [255]. Customer happiness benefits the company in a number of ways, such as through increasing future sales or lowering product returns. In this examination of the literature, a number of studies that aim to please the customer and encourage online apparel shopping have been discovered. A chatbot for buying assistance is still being researched [164,210], and there are also cutting-edge studies to anticipate how clothing will fit [137,149,211] and personalization of recommendations based on style or purchase histories [126,237,238,239]. When examining the sample of chosen articles from a business standpoint, it is important to draw attention to specific studies on demand forecasting, customer purchase intent, and inventory management.

Decathlon, a sports products company with more than 1000 locations globally, was a successful case study. Decathlon launched its online store in the Netherlands, where machine learning technology is being used to analyze and monitor customer behavior in real time and recommend products. As a result, in 2018, their income increased by 10.7%, while the average order value rose by 5.2%. The international jewelry company Pandora is an additional and more recent case study. Virtual Try-On, a web-based augmented reality tool, has been made available across the full inventory as of January 2021. While browsing on their mobile devices, customers can try on a piece of jewelry that is perfectly proportioned [19,256].

6. RQ3: Hot Topics and Upcoming Research Directions in the Field of AI for the e-Commerce Industry of Fashion

After responding to how AI can be actually applied to B2C fashion retail, RQ3 seeks to identify potential future research topics related to e-commerce and artificial intelligence. This analysis identifies four expanding areas where research is anticipated to make progress using the NLR approach.

6.1. Smart City (SC) Oriented e-Commerce

There are several research issues associated with e-commerce of fashion in smart cities that could be explored. AI is a key enabling technology to realize smarter cities and address their challenges [257]. Even though e-commerce was not the primary focus of the smart city paradigm’s initial approach, there are currently several studies that show its significance [258,259,260,261]. Next, we list some research challenges that could be addressed by bringing together the AI and smart city paradigms and the fashion domain:

• Supply chain optimization [262]: Smart cities offer new opportunities for optimizing the fashion supply chain, from sourcing materials to manufacturing and distribution. Indeed, AI and other complementary technologies, e.g., IoT, could be used to improve supply chain efficiency, reduce waste, and improve sustainability.

• Personalization of fashion e-commerce [194]: As smart cities become more connected and data-rich, there is an opportunity to provide more personalized fashion e-commerce experiences. There is a need to keep exploring how AI and machine learning algorithms can be used to recommend products that are tailored to individual consumers based on their preferences, shopping behavior, and location data.

• Customer behavior and preferences [263]: Smart cities generate vast amounts of data about consumer behavior and preferences that can be used to inform fashion e-commerce strategies. Further research should explore how these data can be leveraged to understand consumer trends, predict future demand, and create more targeted marketing campaigns.

6.2. Omnichannel Shopping Experience for Customers

Omnichannel provides consumers with a buying experience that incorporates the benefits of numerous channels into a single customer journey [264,265]. The variety of consumer gadgets makes this task increasingly challenging for retailers. By using the internet and new purchasing technology, many consumers want easier yet richer shopping experiences [266]. For a true omnichannel experience, data must be gathered and analyzed from all channels. For retailers looking to reimagine their businesses using various technologies, such as augmented reality, virtual reality, and mobile applications, SC has some cutting-edge options [267].

6.3. Social Network Information for e-Commerce Marketing

As data sources for the creation of artificial intelligence solutions, social networks are becoming increasingly significant. Social networks are the sources that transmit the perspective of society in real time and have a big impact on how individuals engage with one another. Businesses can now manage information that would normally be impossible to manage or require an excessive amount of time and resources to gather. Social networks have a significant impact on the fashion industry. Networks enable direct communication with customers wherever they are in the retail industry. On the other hand, data analysis enables trend forecasting and the creation of fresh marketing approaches [268,269].

6.4. Matching of Fashion Products

E-commerce has compelled businesses to alter their pricing strategies, ensuring uniform pricing and competitor discounts [270]. Through the use of AI techniques, items that appear to be different but actually refer to the same entity can be automatically identified from various web sources. However, the fashion sector still faces a lot of obstacles for further improvement. Numerous merchants may sell the same clothing items in several markets and languages. Fashion items may have several names, graphics, and descriptions, making their identification more difficult than with other types of items. The categorizing of clothing remains a difficult task. The subjective nature of the perception of garments causes different classes to share many characteristics, making classification a challenging task. A future line of research is suggested to enhance fashion categorization performance due to the nature of the product, using a hybrid approach comparing photos and textual metadata.

7. Validation of Results

Two subject matter experts in the field have reviewed and validated the results obtained in this research. Specifically, an academic expert in the field with experience in the retail B2C sector and an ex-director of a high-end brand that provides athletic clothing with experience in research and development in the retail sector (see Refs. [271,272,273] for further details) were selected for this study. After reviewing this research, they agreed on the results proposed herein, thus validating the developed work.

8. Concluding Remarks

Rapid change is a hallmark of the fashion industry. E-commerce behemoths (such as the aforementioned Decathlon, Pandora, or Amazon) are currently using AI technology to optimize their own e-commerce platforms and boost their level of competition. New AI technologies and methodologies connected to the e-commerce fashion business are promising and are supported by ongoing research advances.

The study’s objective was to carry out an NLR to see what the uses of AI technology in the e-commerce world of fashion are (RQ1), how the fashion sector can use AI to its fullest potential in order to increase customer satisfaction and financial success (RQ2), and what the hot topics and upcoming research directions in the field of AI for the e-commerce industry of fashion are (RQ3). After searching the academic databases Web of Science and Scopus, we found and analyzed 219 articles to answer these questions.

Finding AI applications for the fashion e-commerce industry is the aim of RQ1. A classification taking into account AI techniques was suggested to answer this question: CV, NLP, and other ML applications. The primary applications of CV center on the retrieval of apparel and virtual fitting environments. NLP is mostly utilized for consumer sentiment analysis and recommender system development. Other ML applications, such as profit maximization and sales forecasting, are targeted towards company management control. RQ2 aims at answering how AI could improve business profitability. According to this study, AI is utilized to enhance business management and fulfill consumer experience, which both contribute to increased advantages. Lastly, RQ3 indicates potential future research topics in this area. This analysis identified four expanding areas where new discoveries are anticipated in the future. On the one hand, the effects of e-commerce in SC and users’ omnichannel experiences enable them to easily purchase both online and offline. On the other hand, social media data mining and fashion product matching appear to be some of the most significant issues for the near future. Thus, for scholars drawn to this issue, this research paper offers potential research subjects.

Further research will be oriented to offer a more detailed comparison between different categories of specific techniques: it is clear that all the techniques have their application areas, but it is necessary to go deeper into detail regarding when it is best to apply one or the other considering different criteria. As in all AI disciplines, some techniques are easier to use/implement, some are fast-converging, others are more accurate, etc. Thus, it is essential to analyze their advantages, disadvantages, and applicability limits.

In line with these future orientations, it is worth noting that this study has its limitations. The main restriction this research deals with is how rapid AI itself is changing. These days, all disciplines are changing due to the invasion of open AI. Nevertheless, it was not possible to dedicate a specific section to it because there is a discrepancy about evaluating whether or not a specific AI technology is ‘open’. As this a limitation of the research, even though there are already references in the literature approaching the topic (e.g., Ref. [274]), future work will be oriented to study the potential of open AI as a tool applied to B2C fashion retail. Another limitation of the study is the aforementioned comparison of methods. In this vein, it is necessary to provide statistical analysis that studies how they evolve over time and why some methods seem better than others for a particular class of cases, etc. Regarding the development of the applicability of these methods over time, it is worth noting that most of the references are less than 10 years old (see Figure 2 for details). Therefore, it may be too soon to analyze the temporal evolution of the applied techniques.

Author Contributions

Conceptualization, L.Q.-L., A.G. and A.A.; methodology, L.Q.-L., A.G., A.A., J.G.d.l.P. and D.L.-d.-I.; formal analysis, L.Q.-L.; writing—original draft preparation, L.Q.-L.; writing—review and editing, L.Q.-L., A.G., A.A., J.G.d.l.P. and D.L.-d.-I. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data sharing not applicable.

Acknowledgments

We would like to acknowledge the support received from Sandra Martinez and Ander Errasti, subject matter experts in the retail and logistics areas.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Review methodology. The character * denotes a wildcard.

Figure 2. Number of papers published by year.

Figure 3. Number of articles per topic and subtopic found in the literature review.

Figure 4. Example scheme of a multimodal and crossmodal system.

Figure 5. Fraction of multimodal manuscripts per task type.

Table 1

Final query implemented for filtering the repositories for publications. The * is used as a wildcard character.

Scientific Database	Search String
Web of Science	TS=(((“artificial intelligence” OR “big data” OR “computer vision” OR “data analytics” OR “data mining” OR “deep learning” OR “image processing” OR “machine learning” OR “natural language” OR “neural network” OR “sentiment analy*”) AND (fashion OR cloth* OR textile* ) AND (ecommerce OR e-commerce)))
Scopus	TITLE-ABS-KEY ((“artificial intelligence” OR “big data” OR “computer vision” OR “data analytics” OR “data mining” OR “deep learning” OR “image processing” OR “machine learning” OR “natural language” OR “neural network” OR “sentiment analy*”) AND (fashion OR cloth* OR textile* ) AND (ecommerce OR e-commerce))

Table 2

Databases used.

Database	Frequency
Retailshop	41
Collected	37
CollectedfromtheInternet	31
DeepFashion	21
Collectedfromreviewsofretailshops	19
Collectedfromsocialmedia	10
Fashion-MNIST	10
Collectedfromscanner	4
ImageNet	4
Polyvore	4
Virtualfitting	4
Amazon	3
Kaggle	3
MPV	3
VITON	3
Amazon5-core	2
AmazonDresses	2
DeepFashion3D	2
LookBook	2
Taobao	2
CzechRetailshop	2
Others	30

Table 3

Techniques applied.

Technique/Method	Frequency
CNN	59
BigData	18
DCNN	16
ML	14
NN	12
Survey	11
GAN	9
DL	8
Imageprocessing	7
kNN	7
randomforest	7
Review	6
DNN	5
LSTM	5
NaiveBayes	5
Siamesenetwork	5
Decisiontree	4
k-means	4
LAC	4
SVM	4
Word2vec	4
Collaborativefiltering	3
CorrelationalNN	3
Fuzzylogic	3
GraphCNN	3
BERT	2
Classificationalgorithms	2
CNNLSTM	2
DART	2
GaussianMixtureModels	2
H-CNN	2
Kneser–Ney	2
LR	2
RBFSVM	2
Regressionmodels	2
SSD	2
Others	56

Table 4

Specific purpose.

Technique/Method	Frequency
CNN	59
BigData	18
DCNN	16
ML	14
NN	12
Survey	11
GAN	9
DL	8
Imageprocessing	7
kNN	7
randomforest	7
Review	6
DNN	5
LSTM	5
NaiveBayes	5
Siamesenetwork	5
Decisiontree	4
k-means	4
LAC	4
SVM	4
Word2vec	4
Collaborativefiltering	3
CorrelationalNN	3
Fuzzylogic	3
GraphCNN	3
BERT	2
Classificationalgorithms	2
CNNLSTM	2
DART	2
GaussianMixtureModels	2
H-CNN	2
Kneser–Ney	2
LR	2
RBFSVM	2
Regressionmodels	2
SSD	2
Others	56

Table 5

Summary of methods with references.

Methods	Citations
ALS	[235]
associationrules	[247]
AttentionCNN	[163]
AttentionDeepLearning	[161]
Bayesiannetworks	[227]
BEAM	[167]
BERT	[44,78]
BigData	[177,189,192,193,194,198,199,200,205,206,212,214,216,225,226,227,241]
BPR	[235]
CF	[247]
Classificationalgorithms	[217]
CNN	[46,47,48,49,52,57,58,60,61,62,66,68,69,70,72,74,76,78,81,82,83,91,93,96,97,98,100,101,102,104,105,112,123,128,137,140,145,154,156,158,166,250,251,252,253]
CNNLSTM	[42,169]
Collaborativefiltering	[238,240,249]
CollaborativefilteringCNN	[243]
CorrelationalNN	[73,95]
DART	[33]
DCNN	[54,55,63,86,88,90,103,108,111,159,170,254]
Decisiontree	[42,131,179,220]
Deformationalgorithm	[151]
DL	[50,134,135,195,211,241,245]
DNN	[84,183,187,246]
Domaindictionary	[178]
DPM	[110]
ELR	[201]
FastkNN	[152]
FCNN	[56]
FNN	[32]
Fuzzylogic	[106,113]
GAN	[71,75,99,129,134,141,155,251]
GA-RF	[65]
GaussianMixtureModels	[109,118]
gradientboostingdecisiontree	[42]
GraphCNN	[40,122]
GraphDCNN	[89]
GRU	[230]
GSN	[87]
H-CNN	[39,157]
HOG	[153]
HypergraphNN	[80]
Imageprocessing	[119,120,130,132,135,136,147]
k-center	[117]
k-means	[114,203,232,244]
k-medoids	[115]
Kneser–Ney	[34]
kNN	[54,137,222,235,237,250]
LAC	[107,116]
Lassoregression	[222]
LDA	[160]
LightGBM	[173]
LinearSVC	[182]
logisticregression	[42]
LR	[179,220]
LSTM	[85,126,163,171,176]
MDNN	[77]
ML	[130,132,133,136,185,197,209,223,224,228,229,234,236,244]
Modularontology	[184]
MT-GAN	[67]
MultimodalNN	[124]
NaiveBayes	[149,165,176,179,220]
NN	[94,144,164,168,174,195,202,208,213,218]
OLS	[222]
Pareto	[209]
Pareto/NBD	[207]
PCA-SVD	[125]
randomforest	[33,42,186,215,220,231]
RBFSVM	[175,220]
RCNN	[51]
R-CNN	[59]
Recommender	[201]
Regressionmodels	[142,174]
RepTree	[203]
review	[121,138,188,190,204,248]
RFM	[207]
R-GCN	[242]
RNN	[80]
Robotandpressuremeasurements	[146]
SEM	[239]
siamesenetwork	[46,75,79,156]
SSD	[35,92]
Survey	[53,127,143,150,162,181,191,196,210,219,221]
Survey:kanomodel	[139]
SVM	[64,87,153,179]
SVM.REPTree	[172]
SVP	[222]
UCB	[233]
VAR	[207]
VGG-IE	[65]
Viola–Jones	[148]
word2vec	[98,102,167]
word2vecSVMperf	[180]
XGBoost	[176]

Table 6

Summary of purposes with references.

Purpose	Citations
Clothesclassification	[34,39,46,47,48,49,52,56,57,58,60,61,62,63,65,67,68,69,74,76,82,83,84,89,90,91,92,93,96,101,105,107,110,111,114,116,118]
Customerbehavior	[33,210,211,212,213,214,215,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249]
Datalabeling	[55,61,94,95,98,104,107,111,113,116,181,182,183,184]
Itemretrieval	[40,50,51,53,54,55,59,62,64,70,71,73,75,77,81,84,85,86,87,88,94,95,98,99,100,101,102,103,104,106,112,113,115,117,119,120,153,154]
Landmarkdetection	[35,66,72,97,108,109]
Management	[33,177,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,202,203,204,205,206,207,208,209,216,217]
Recommender	[34,78,79,80,81,121,122,123,124,125,126,127,128,155,156,157,158,159,160,161,162,163,164,165,166,167,168,201,202,250,251,252,253,254]
Sentimentanalysis	[42,44,169,170,171,172,173,174,175,176,177,178,179,180]
Virtualfitting	[32,129,130,131,132,133,134,135,136,138,139,140,141,142,143,144,145,146,149,150,151,152]

Table 7

Summary of databases with references.

Databases	Citations
ACS	[83]
AdidasAG	[82]
Amazon	[34]
amazon.com	[158]
Amazon5-core	[163]
AmazonDresses	[94]
AmazonFashion	[122]
Amazonfashiondataset	[73]
ane-commerceplatform	[50]
ASOS	[124]
CCP	[97]
Clothing1M	[70]
Collar-6	[49]
Collected	[76,91,92,93,96,98,100,110,112,114,136,137,145,149,151,152,201,223,224,227,230,231,235,236,245,247,251]
Collectedfromreviewsofretailshops	[42,44,160,165,169,170,174,178,179,180,183]
Collectedfromscanner	[46,132]
Collectedfromsocialmedia	[107,116,172,175,222]
CollectedfromtheInternet	[81,95,102,104,117,128,153,155,167,168,176,177,192,203,225,226,228,234,238,252]
CollectedfromtheInternetandsocialmedia	[206]
Colorful-Fashion	[35]
CzechretailshopsfromtheInternet	[54]
DARN	[103]
DeepFashion	[48,52,55,62,64,67,69,77,79,84,89,101,103,123,154,250]
DeepFashion2	[154]
DeepFashion3D	[135]
DeepFashion-C	[57]
DressCode	[129]
FashionAI2018	[72]
FashionDNA	[88]
Fashionista	[108]
FashionLandmarkdetection	[57]
FashionMNIST	[39,74,87]
Fashion-MNIST	[58,62,65,68]
FashionVC	[166]
Feidegger	[123]
FindFashion	[40]
GoogleAnalytics	[212]
ImageNet	[47,56,63]
Image-Net	[157]
iPER	[141]
Kaggle	[60,125,173]
LookBook	[75]
MovingFashion	[51]
MPV	[134,140]
POG	[159]
Polyvore	[78,126]
PolyvoreMayland	[166]
retailshop	[33,156,185,186,187,195,196,198,199,202,207,208,211,214,215,216,229,232,246,254]
RetailshopfromtheInternet	[218,237]
RetailshopsfromtheInternet	[71,99,105,109,111,144,182,189,209]
Street2Shop	[79]
Taobao	[80]
TaobaoiFashion	[122]
ThePraguetexturesegmentationdata-generatorandbenchmark	[118]
Tianchi	[159]
Virtualfitting	[137,147,148]
VITON	[134,140]
WFID	[86]

Table 8

Summary of datasets in the reviewed articles.

Dataset	Year	Task	Key Features	Source
Fashionista	2012	garment labeling	158k images, annotated with tags, comments, links	chitopia.com, accessed on 1 June 2023
ACS	2013	clothes classification	80,000 images, 15 types of clothes	shopping websites
CCP	2014	garment labeling	2k high-resolution street fashion photos	shopping websites
Colorful-Fashion	2014	garment labeling	2k 600 × 400 images, annotated with 13 colors	chitopia.com, accessed on 1 June 2023
DARN	2015	clothes retrieval	545k images, annotated upper clothing image pair	shopping websites
DeepFashion	2016	landmark detection	800k images (categories, attributes, landmarks)	Forever21, Mogujie
DeepFashion-C	2016	landmark detection	289k images, annotated with bounding box pose variation, category, and attributes	shopping websites and Google
FLD	2016	landmark detection	123k images annotated, clothing type and pose variation type	Deep Fashion
LookBook	2016	clothes retrieval	84k images; 75k images are associated with 10k top product images	Bongjashop, Jogunshop, Stylenanda, SmallMan, WonderPlace
Clothing1M	2017	clothes classification	1 million images in 14 classes	shopping websites
Fashion-MNIST	2017	clothes classification	70k images, 28 × 28 greyscale images, 10 classes	Zalando
Amazon 5-core	2018	sentiment analysis	41 million reviews, in which all users and items have at least 5 reviews	amazon.com, accesed on 1 June 2023
Feidegger	2018	text–image retrieval	8k images of dresses, each image with 5 textual annotations in German	Zalando
ExpFashion	2018	clothes recommendation	853k outfits; outfits consist of: one top and one bottom piece	polyvore.com, accesed on 1 June 2023
Polyvore68K	2018	clothes recommendation	Polyvore68K-ND and Polyvore68K-D, 175k items	polyvore.com, accesed on 1 June 2023
VITON	2018	virtual try on	32k pairs of frontal view women and top clothing images
Womens e-commerce clothing reviews	2018	sentiment analysis	23k customer reviews and ratings
DeepFashion2	2019	parsing, landmark detection, retrieval, pose estimation	491k images	DeepFashion, shopping websites
FindFashion	2019	clothes retrieval	565k images, merges two existing datasets	Steet2Shop, DeepFashion
iPER	2019	virtual try on	206 video sequences, 30 subjects in random actions, 103 clothes
Amazon Fashion	2020	clothes retrieval	53k images with text description	amazon.com, accesed on 1 June 2023
MPV	2020	virtual try on	37k/14k people/clothes images; person with different poses
Tianchi	2020	recommendation	28k user profiles and 2.8 million records of purchase behavior	Alypay

References

1. Emarketer. Global Ecommerce Update 2021—Insider Intelligence Trends, Forecasts & Statistics. 2021; Available online: https://www.emarketer.com/content/global-ecommerce-update-2021 (accessed on 28 October 2021).

2. Statista. Global Retail e-Commerce Market Size 2014–2023. 2019; Available online: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ (accessed on 28 October 2021).

3. Sheth, J. Impact of COVID-19 on Consumer behavior: Will the old habits return or die?. J. Bus. Res.; 2020; 117, pp. 280-283. [DOI: https://dx.doi.org/10.1016/j.jbusres.2020.05.059]

4. Roggeveen, A.L.; Sethuraman, R. How the COVID-19 Pandemic May Change the World of Retailing. J. Retail.; 2020; 96, pp. 169-171. [DOI: https://dx.doi.org/10.1016/j.jretai.2020.04.002]

5. Statista. Fashion eCommerce Report 2020. 2021; Available online: https://www.statista.com/study/38340/ecommerce-report-fashion/ (accessed on 25 October 2021).

6. Rehouma, R.; Buchert, M.; Chen, Y.-P.P. Machine learning for medical imaging-based COVID-19 detection and diagnosis. Int. J. Intell. Syst.; 2021; 36, pp. 5085-5115. [DOI: https://dx.doi.org/10.1002/int.22504]

7. Muhammad, K.; Khan, S.; Ser, J.D.; Albuquerque, V.H.C.D. Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey. IEEE Trans. Neural. Netw. Learn Syst.; 2020; 32, pp. 507-522. [DOI: https://dx.doi.org/10.1109/TNNLS.2020.2995800] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32603291]

8. Olsen, T.L.; Tomlin, B. Industry 4.0: Opportunities and challenges for operations management. Manuf. Serv. Oper. Manag.; 2020; 22, pp. 113-122. [DOI: https://dx.doi.org/10.1287/msom.2019.0796]

9. Deebak, B.; Al-Turjman, F. Digital-twin assisted: Fault diagnosis using deep transfer learning for machining tool condition. Int. J. Intell. Syst.; 2021; 37, pp. 10289-10316. [DOI: https://dx.doi.org/10.1002/int.22493]

10. Bennajeh, A.; Ben Said, L. Driving control based on bilevel optimization and fuzzy logic. Int. J. Intell. Syst.; 2021; 36, pp. 4495-4523. [DOI: https://dx.doi.org/10.1002/int.22441]

11. Boukerche, A.; Tao, Y.; Sun, P. Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Comput. Netw.; 2020; 182, 107484. [DOI: https://dx.doi.org/10.1016/j.comnet.2020.107484]

12. Pillai, R.; Sivathanu, B.; Dwivedi, Y.K. Shopping intention at AI-powered automated retail stores (AIPARS). J. Retail. Consum. Serv.; 2020; 57, 102207. [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102207]

13. Xu, J.; Hu, Z.; Zou, Z.; Zou, J.; Hu, X.; Liu, L.; Zheng, L. Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence. IEEE Access; 2020; 8, pp. 147728-147737. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3014047]

14. Saumya, S.; Singh, J.P.; Dwivedi, Y.K. Predicting the helpfulness score of online reviews using convolutional neural network. Soft Comput.; 2020; 24, pp. 10989-11005. [DOI: https://dx.doi.org/10.1007/s00500-019-03851-5]

15. Koehn, D.; Lessmann, S.; Schaal, M. Predicting online shopping behaviour from clickstream data using deep learning. Expert Syst. Appl.; 2020; 150, 113342. [DOI: https://dx.doi.org/10.1016/j.eswa.2020.113342]

16. Moriuchi, E.L.; Ers, V.M.; Colton, D.; Hair, N. Engagement with chatbots versus augmented reality interactive technology in e-commerce. J. Strateg. Mark.; 2021; 29, pp. 375-389. [DOI: https://dx.doi.org/10.1080/0965254X.2020.1740766]

17. Zhou, L. Product advertising recommendation in e-commerce based on deep learning and distributed expression. Electron. Commer. Res.; 2020; 20, pp. 321-342. [DOI: https://dx.doi.org/10.1007/s10660-020-09411-6]

18. John, D. Types of Literature Review–Research–Methodology. Business Research Methodology. 2020; Available online: https://research-methodology.net/research-methodology/types-literature-review/ (accessed on 15 May 2023).

19. Tangiblee. Pandora Jewelry expands Virtual Try-On to more categories|Retail Dive. Retail Dive. 2021; Available online: https://www.retaildive.com/press-release/20210315-pandora-jewelry-expands-virtual-try-on-to-more-categories/ (accessed on 19 April 2023).

20. Green, B.N.; Johnson, C.D.; Adams, A. Writing narrative literature reviews for peer-reviewed journals: Secrets of the trade. J. Chiropr. Med.; 2006; 5, 101. [DOI: https://dx.doi.org/10.1016/S0899-3467(07)60142-6]

21. Ferrari, R. Writing narrative style literature reviews. Eur. Med. Writ. Assoc.; 2015; 24, pp. 230-235. [DOI: https://dx.doi.org/10.1179/2047480615Z.000000000329]

22. Ogunmakinde, O.E.; Egbelakin, T.; Sher, W. Contributions of the circular economy to the UN sustainable development goals through sustainable construction. Resour. Conserv. Recycl.; 2022; 178, 106023. [DOI: https://dx.doi.org/10.1016/j.resconrec.2021.106023]

23. Hettithanthri, U.; Hansen, P. Design studio practice in the context of architectural education: A narrative literature review. Int. J. Technol. Des. Educ.; 2022; 32, pp. 2343-2364. [DOI: https://dx.doi.org/10.1007/s10798-021-09694-2]

24. Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA, 2016.

25. Bhatia, N. Others Survey of nearest neighbor techniques. arXiv; 2010; arXiv: 1007.0085

26. Kingsford, C.; Salzberg, S. What are decision trees?. Nat. Biotechnol.; 2008; 26, pp. 1011-1013. [DOI: https://dx.doi.org/10.1038/nbt0908-1011]

27. Webb, G.; Keogh, E.; Miikkulainen, R. Naive Bayes. Encyclopedia of Machine Learning; Springer: Berlin/Heidelberg, Germany, 2010; Volume 15, pp. 713-714.

28. Noble, W. What is a support vector machine?. Nat. Biotechnol.; 2006; 24, pp. 1565-1567. [DOI: https://dx.doi.org/10.1038/nbt1206-1565]

29. Ahmed, M.; Seraj, R.; Islam, S. The k-means algorithm: A comprehensive survey and performance evaluation. Electronics; 2020; 9, 1295. [DOI: https://dx.doi.org/10.3390/electronics9081295]

30. Koren, Y.; Rendle, S.; Bell, R. Advances in collaborative filtering. Recommender Systems Handbook; Springer: New York, NY, USA, 2021; pp. 91-142.

31. Reynolds, D. Others Gaussian mixture models. Encyclopedia of Biometrics; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2009; Volume 741.

32. Wang, R.; Du, H.; Zhou, F.; Deng, D.; Liu, Y. An adaptive neural fuzzy network clothing comfort evaluation model and application in digital home. Multimed Tools Appl.; 2014; 71, pp. 395-410. [DOI: https://dx.doi.org/10.1007/s11042-013-1519-4]

33. Hofmann, A.; Gwinner, F.; Fuchs, K.; Winkelmann, A. An industry-agnostic approach for the prediction of return shipments. Proceedings of the 26th Americas Conference on Information Systems, AMCIS 2020; Virtual, 15–17 August 2020.

34. Sorokina, D.; Cantu-Paz, E.; Cantú-Paz, E. Amazon Search: The Joy of Ranking Products. Proceedings of the 39th International ACM Sigir Conference on Research and Development in Information Retrieval; Pisa, Italy, 17–21 July 2016; pp. 459-460. [DOI: https://dx.doi.org/10.1145/2911451.2926725]

35. Zhong, L. Edge-based fashion detection by transfer learning. Proceedings of the 5th International Conference on Mechanical, Control and Computer Engineering, ICMCCE 2020; Harbin, China, 25–27 December 2020; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2020; pp. 2132-2136. [DOI: https://dx.doi.org/10.1109/ICMCCE51767.2020.00464]

36. Dong, S.; Wang, P.; Abbas, K. A survey on deep learning and its applications. Comput. Sci. Rev.; 2021; 40, 100379. [DOI: https://dx.doi.org/10.1016/j.cosrev.2021.100379]

37. Bharati, P.; Pramanik, A. Deep learning techniques—R-CNN to mask R-CNN: A survey. Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019; Springer: Singapore, 2020; pp. 657-668.

38. Abdelouahab, K.; Pelcat, M.; Serot, J.; Berry, F. Accelerating CNN inference on FPGAs: A survey. arXiv; 2018; arXiv: 1806.01683

39. Seo, Y.; Shin, K.-S. Hierarchical convolutional neural networks for fashion image classification. Expert Syst. Appl.; 2019; 116, pp. 328-339. [DOI: https://dx.doi.org/10.1016/j.eswa.2018.09.022]

40. Kuang, Z.; Gao, Y.; Li, G.; Luo, P.; Chen, Y.; Lin, L.; Zhang, W. Fashion retrieval via graph reasoning networks on a similarity pyramid. IEEE Int. Conf. Comput. Vis.; 2019; 45, pp. 3066-3075. [DOI: https://dx.doi.org/10.1109/ICCV.2019.00316]

41. Lindemann, B.; Maschler, B.; Sahlab, N.; Weyrich, M. A survey on anomaly detection for technical systems using LSTM networks. Comput. Ind.; 2021; 131, 103498. [DOI: https://dx.doi.org/10.1016/j.compind.2021.103498]

42. Fang, Z. Enhanced Customer Analysis Based on Variations of Natural Language Processing Algorithms Implemented on Past E-Commerce Reviews. Proceedings of the ACM International Conference Proceeding Series; Guangzhou, China, 18–20 June 2021; pp. 202-212. [DOI: https://dx.doi.org/10.1145/3473714.3473748]

43. Al-Saqqa, S.; Awajan, A. The use of word2vec model in sentiment analysis: A survey. Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control; Majorca Island, Spain, 3–5 May 2019; pp. 39-43.

44. Yu, L.; Bai, X. Implicit Aspect Extraction from Online Clothing Reviews with Fine-tuning BERT Algorithm. J. Phys. Conf. Ser.; 2021; 1995, 012040. [DOI: https://dx.doi.org/10.1088/1742-6596/1995/1/012040]

45. Chicco, D. Siamese neural networks: An overview. Artif. Neural Netw.; 2021; 2190, pp. 73-94.

46. Sonawane, C.; Singh, D.P.; Sharma, R.; Nigam, A.; Bhavsar, A. Fabric Classification and Matching Using CNN and Siamese Network for E-commerce. Proceedings of the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019. Vol 11679 LNCS; Salerno, Italy, 3–5 September 2019; pp. 193-205. [DOI: https://dx.doi.org/10.1007/978-3-030-29891-3_18]

47. Xiang, J.; Pan, R.; Gao, W. Clothing recognition based on deep sparse convolutional neural network. Int. J. Cloth. Sci. Technol.; 2022; 34, pp. 119-133. [DOI: https://dx.doi.org/10.1108/IJCST-06-2018-0081]

48. Zhou, X.; Li, H.; Zhang, D. Automatic fabric pattern recognition and design based on deep learning and portable device. Internet Technol. Lett.; 2022; e343. [DOI: https://dx.doi.org/10.1002/itl2.343]

49. Chengcheng, H.; Jian, Y.; Xiao, Q. Research and Application of Fine-Grained Image Classification Based on Small Collar Dataset. Front. Comput. Neurosci.; 2022; 15, 121. [DOI: https://dx.doi.org/10.3389/fncom.2021.766284] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35480229]

50. Parekh, V.; Mathur, S.; Biswas, S.; Shaik, K. VPER: Visual Product Entity Recognition for Fashion-wear. Proceedings of the ACM International Conference Proceeding Series; Lyon, France, 25–29 April 2022; pp. 270-274. [DOI: https://dx.doi.org/10.1145/3493700.3493739]

51. Godi, M.; Joppi, C.; Skenderi, G.; Cristani, M. MovingFashion: A Benchmark for the Video-to-Shop Challenge. Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022; Waikoloa, HI, USA, 4–8 January 2022; pp. 517-525. [DOI: https://dx.doi.org/10.1109/WACV51458.2022.00059]

52. Zhou, H.; Peng, Z.; Tao, R.; Zhang, L. Feature Fusion Multi_XMNet Convolution Neural Network for Clothing Image Classification. J. Donghua Univ. (Eng. Ed.); 2021; 38, pp. 519-526. [DOI: https://dx.doi.org/10.19884/j.1672-5220.202101013]

53. Li, X.; Yang, J.; Ma, J. Recent developments of content-based image retrieval (CBIR). Neurocomputing; 2021; 452, pp. 675-689. [DOI: https://dx.doi.org/10.1016/j.neucom.2020.07.139]

54. Grošup, T.; Peška, L.; Skopal, T. On augmenting database schemas by latent visual attributes. Knowl. Inf. Syst.; 2021; 63, pp. 2277-2312. [DOI: https://dx.doi.org/10.1007/s10115-021-01595-z]

55. Yue, X.; Zhang, C.; Fujita, H.; Lv, Y. Clothing fashion style recognition with design issue graph. Appl Intell.; 2021; 51, pp. 3548-3560. [DOI: https://dx.doi.org/10.1007/s10489-020-01950-7]

56. Mohammad, M.N.; Kumari, C.U.; Murthy, A.S.D.; Jagan, B.O.L.; Saikumar, K. Implementation of online and offline product selection system using FCNN deep learning: Product analysis. Mater. Today Proc.; 2021; 45, pp. 2171-2178. [DOI: https://dx.doi.org/10.1016/j.matpr.2020.10.072]

57. Shajini, M.; Ramanan, A. An improved landmark-driven and spatial–channel attentive convolutional neural network for fashion clothes classification. Vis. Comput.; 2020; 37, pp. 1517-1526. [DOI: https://dx.doi.org/10.1007/s00371-020-01885-7]

58. Kumar, T.R.; Kishor, S.; Kishore, R.U.; Kumar, C.N. Convoluted fashion classifier prediction using Neural Networks. J. Phys. Conf. Ser.; 2021; 1916, 012029. [DOI: https://dx.doi.org/10.1088/1742-6596/1916/1/012029]

59. Huang, Q.; Han, X.; Lu, T.; Liu, G. Clothing Image Retrieval Based on Parts Detection and Segmentation. Proceedings of the 3rd International Conference Image Process Mach Vision, IPMV 2021; Hong Kong, China, 22–24 May 2021; pp. 53-59. [DOI: https://dx.doi.org/10.1145/3469951.3469961]

60. Jha, B.K.; Sivasankari, G.G.; Venugopal, K.R. E-Commerce Product Image Classification using Transfer Learning. Proceedings of the 5th International Conference on Computing Methodologies and Communication, ICCMC 2021; Erode, India, 8–10 April 2021; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2021; pp. 904-912. [DOI: https://dx.doi.org/10.1109/ICCMC51019.2021.9418371]

61. Qiu, B.; Liu, X.; Shi, Y.; Xin, B. Shirt Semantic Classification Based on Convolutional Neural Network. J. Phys. Conf. Ser.; 2021; 1790, 012097. [DOI: https://dx.doi.org/10.1088/1742-6596/1790/1/012097]

62. Itkare, S.; Manjaramkar, A. Fashion Classification and Object Detection Using CNN. Lect. Notes Netw. Syst.; 2021; 190, pp. 227-236. [DOI: https://dx.doi.org/10.1007/978-981-16-0882-7_18]

63. Elleuch, M.; Mezghani, A.; Khemakhem, M.; Kherallah, M. Clothing Classification Using Deep Cnn Architecture Based on Transfer Learning. Proceedings of the Hybrid Intelligent Systems: 19th International Conference on Hybrid Intelligent Systems (HIS 2019); Bhopal, India, 10–12 December 2019; [DOI: https://dx.doi.org/10.1007/978-3-030-49336-3_24]

64. Pereira, A.M.; Vieira, T.; de Barros Costa, E. Balancing exploration and exploitation: An image-based approach to item retrieval with enhanced diversity. Comput. Electr. Eng.; 2020; 84, 106605. [DOI: https://dx.doi.org/10.1016/j.compeleceng.2020.106605]

65. Liu, J.; Zheng, Y.; Dong, K.; Yu, H.; Zhou, J.; Jiang, Y.; Jiang, Z.; Guo, S.; Ding, R. Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm. Int. J. Pattern. Recognit. Artif. Intell.; 2020; 34, 2051004. [DOI: https://dx.doi.org/10.1142/S0218001420510040]

66. Saini, H.; Thakkar, V.; Dasani, R.; Yu, J.Y. Detecting Fashion Apparels and their Landmarks. Proceedings of the 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology; Melbourne, Australia, 14–17 December 2021; pp. 946-953. [DOI: https://dx.doi.org/10.1109/WIIAT50758.2020.00145]

67. Li, Q.; Hu, C.; Chang, K.H.; Zhang, R. Boosting Fashion Image Attributes Classification Performance with MT-GAN Training Technique. Proceedings of the 7th International Conference on Data Science and Advanced Analytics, DSAA 2020; Sydney, Australia, 6–9 October 2020; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2020; pp. 400-409. [DOI: https://dx.doi.org/10.1109/DSAA49011.2020.00054]

68. Meshkini, K.; Platos, J.; Ghassemain, H. An Analysis of Convolutional Neural Network for Fashion Images Classification (Fashion-MNIST). Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry”(IITI’19); Ostrava–Prague, Czech Republic, 2–7 December 2019; Springer: Cham, Switzerland, 2020; [DOI: https://dx.doi.org/10.1007/978-3-030-50097-9_10]

69. Dhariwal, S.; Liu, Y.; Karali, A.; Vlassov, V. Clothing Classification using Unsupervised Pre-Training. Proceedings of the 4th International Conference on Multimedia Computing, Networking and Applications, MCNA 2020; Valencia, Spain, 19–22 October 2020; pp. 82-89. [DOI: https://dx.doi.org/10.1109/MCNA50957.2020.9264287]

70. Li, Q.; Peng, X.; Cao, L.; Du, W.; Xing, H.; Qiao, Y.; Peng, Q. Product image recognition with guidance learning and noisy supervision. Comput. Vis. Image Underst.; 2020; 196, 102963. [DOI: https://dx.doi.org/10.1016/j.cviu.2020.102963]

71. Zhang, H.; Sun, Y.; Liu, L.; Xu, X. CascadeGAN: A category-supervised cascading generative adversarial network for clothes translation from the human body to tiled images. Neurocomputing; 2020; 382, pp. 148-161. [DOI: https://dx.doi.org/10.1016/j.neucom.2019.11.085]

72. Li, Q.; Yao, L.; Guan, X. Clothing Key Points Detection Algorithm Based on Cascade Convolutional Neural Network. J. Tianjin Univ. Sci. Technol.; 2020; 53, pp. 229-236. [DOI: https://dx.doi.org/10.11784/tdxbz201901054]

73. Yan, C.; Li, Y.; Wan, Y.; Zhang, Z. Joint Image-text Representation Learning for Fashion Retrieval. Proceedings of the ACM International Conference Proceeding Series; Denton, TX, USA, 3–4 October 2020; pp. 412-417. [DOI: https://dx.doi.org/10.1145/3383972.3384048]

74. Kayed, M.; Anter, A.; Mohamed, H. Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture. Proceedings of the 2020 International Conference on Innovative Trends in Communication and Computer Engineering, ITCE 2020; Aswan, Egypt, 8–9 February 2020; pp. 238-243. [DOI: https://dx.doi.org/10.1109/ITCE48509.2020.9047776]

75. Khaund, A.; Thapar, D.; Nigam, A. PoshakNet: Framework for Matching Dresses from Real-Life Photos Using GAN and Siamese Network; Springer: Singapore, 2020; Volume 1249, [DOI: https://dx.doi.org/10.1007/978-981-15-8697-2_11]

76. Qin, X.; Huang, C.; Wu, J.; Yuan, C. A Classification Algorithm for Real Collar Images. Proceedings of the 16th International Conference on Intelligent Computing, ICIC 2020; Bari, Italy, 2–5 October 2020; Volume 12463, pp. 355-366. [DOI: https://dx.doi.org/10.1007/978-3-030-60799-9_31]

77. Ly, N.Q.; Do, T.K.; Nguyen, B.X. Large-Scale Coarse-to-Fine Object Retrieval Ontology and Deep Local Multitask Learning. Comput. Intell. Neurosci.; 2019; 2019, 1483294. [DOI: https://dx.doi.org/10.1155/2019/1483294]

78. Wang, R.; Wang, J.; Su, Z. Learning compatibility knowledge for outfit recommendation with complementary clothing matching. Comput. Commun.; 2022; 181, pp. 320-328. [DOI: https://dx.doi.org/10.1016/j.comcom.2021.10.022]

79. Wróblewska, A.; Dąbrowski, J.; Pastuszak, M.; Michałowski, A.; Daniluk, M.; Rychalska, B.; Wieczorek, M.; Sysko-Romańczuk, S. Designing Multi-Modal Embedding Fusion-Based Recommender. Electron; 2022; 11, 1391. [DOI: https://dx.doi.org/10.3390/electronics11091391]

80. Yuan, J.; Li, Z.; Zou, P.; Gao, X.; Pan, J.; Ji, W.; Wang, X. Community trend prediction on heterogeneous graph in e-commerce. Proceedings of the WSDM 2022—Proceedings of the 15th ACM International Conference on Web Search and Data Mining; Singapore, 12 August 2022; pp. 1319-1327. [DOI: https://dx.doi.org/10.1145/3488560.3498522]

81. Zhou, W.; Mok, P.Y.; Zhou, Y.; Zhou, Y.; Shen, J.; Qu, Q.; Chau, K.P. Fashion recommendations through cross-media information retrieval. J. Vis. Commun. Image Represent.; 2019; 61, pp. 112-120. [DOI: https://dx.doi.org/10.1016/j.jvcir.2019.03.003]

82. Donati, L.; Iotti, E.; Mordonini, G.; Prati, A. Fashion product classification through deep learning and computer vision. Appl. Sci.; 2019; 9, 1385. [DOI: https://dx.doi.org/10.3390/app9071385]

83. Gulbas, B.; Sengur, A.; Incel, E.; Akbulut, Y. Deep features and extreme learning machines based apparel classification. Proceedings of the 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019; Malatya, Turkey, 21–22 September 2019; [DOI: https://dx.doi.org/10.1109/IDAP.2019.8875916]

84. Boriya, A.; Malla, S.S.; Manjunath, R.; Velicheti, V.; Eirinaki, M. ViSeR: A visual search engine for e-retail. Proceedings of the 2019 1st International Conference on Transdisciplinary AI, TransAI 2019; Laguna Hills, CA, USA, 25–27 September 2019; pp. 76-83. [DOI: https://dx.doi.org/10.1109/TransAI46475.2019.00021]

85. Luo, Z.; Yuan, J.; Yang, J.; Wen, W. Spatial Constraint Multiple Granularity Attention Network for Clothesretrieval. Proceedings of the International Conference on Image Processing, ICIP; Taipei, Taiwan, 22–25 September 2019; pp. 859-863. [DOI: https://dx.doi.org/10.1109/ICIP.2019.8802938]

86. Xiang, J.; Zhang, N.; Pan, R.; Gao, W. Fabric Image Retrieval System Using Hierarchical Search Based on Deep Convolutional Neural Network. IEEE Access; 2019; 7, pp. 35405-35417. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2898906]

87. Chopra, A.; Sinha, A.; Gupta, H.; Sarkar, M.; Ayush, K.; Krishnamurthy, B. Powering robust fashion retrieval with information rich feature embeddings. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Long Beach, CA, USA, 16–17 June 2019; pp. 326-334. [DOI: https://dx.doi.org/10.1109/CVPRW.2019.00045]

88. Lasserre, J.; Bracher, C.; Vollgraf, R. Street2Fashion2Shop: Enabling Visual Search in Fashion e-Commerce Using Studio Images. Pattern Recognition Applications and Methods; De Marsico, M.; di Baja, G.; Fred, A. ICPRAM 2018; Lecture Notes in Computer Science Springer: Cham, Switzerland, 2019; Volume 11351, [DOI: https://dx.doi.org/10.1007/978-3-030-05499-1_1]

89. Zhang, C.; Yue, X.; Liu, W.; Gao, C. Fashion style recognition with graph-based deep convolutional neural networks. Proceedings of the Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018; Shanghai, Chian, 25–27 November 2019; Volume 849, pp. 269-275. [DOI: https://dx.doi.org/10.1007/978-3-319-99695-0_32]

90. He, J.; Jia, X.; Li, J.; Yu, S.; Shen, L. Fine-Grained Apparel Image Recognition Based on Deep Learning. Artificial Intelligence on Fashion and Textiles; Wong, W. AITA 2018; Advances in Intelligent Systems and Computing Springer: Cham, Switzerland, 2019; Volume 849, [DOI: https://dx.doi.org/10.1007/978-3-319-99695-0_21]

91. Huang, J.; Wu, X.; Zhu, J.; He, R. Real-Time Clothing Detection with Convolutional Neural Network. Recent Developments in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2017; Springer: Singapore, 2019; Volume 752, [DOI: https://dx.doi.org/10.1007/978-981-10-8944-2_28]

92. Liu, Z. A Deep Learning Method for Suit Detection in Images. Proceedings of the International Conference on Signal Processing Proceedings, ICSP; Trondheim, Norway, 29 July–2 August 2019; pp. 439-444. [DOI: https://dx.doi.org/10.1109/ICSP.2018.8652452]

93. Li, Q.-Q.; Zhong, Y.-Q.; Wang, X. Female apparel classification based on convolutional neural network. Proceedings of the 11th Textile Bioengineering and Informatics Symposium, TBIS 2018; Manchester, UK, 25–28 July 2018; pp. 575-581.

94. Laenen, K.; Zoghbi, S.; Moens, M.F. Web search of fashion items with multimodal qerying. Proceedings of the 11th ACM International Conference on Web Search and Data Mining; Marina Del Rey, CA, USA, 5–9 February 2018; pp. 342-350. [DOI: https://dx.doi.org/10.1145/3159652.3159716]

95. Saha, A.; Nawhal, M.; Khaprat, M.M.; Raykar, V.C. Learning Disentangled Multimodal Representations for the Fashion Domain. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV 2018); Lake Tahoe, NV, USA, 12–15 March 2018; pp. 557-566. [DOI: https://dx.doi.org/10.1109/WACV.2018.00067]

96. Li, Q.-Q.; Zhong, Y.-Q.; Wang, X. Classification of Female Apparel using Convolutional Neural Network. J. Fiber. Bioeng. Inform.; 2018; 11, pp. 209-216. [DOI: https://dx.doi.org/10.3993/jfbim00319]

97. Feng, Z.; Luo, X.; Yang, T.; Kita, K. An object detection system based on YOLOv2 in fashion apparel. Proceedings of the 4th International Conference on Computer and Communications, ICCC 2018; Seattle, WA, USA, 27 July 2018; pp. 1532-1536. [DOI: https://dx.doi.org/10.1109/CompComm.2018.8780944]

98. Rubio, A.; Yu, L.; Simo-Serra, E.; Moreno-Noguer, F. Multi-modal joint embedding for fashion product retrieval. Proceedings of the International Conference on Image Processing, ICIP; Athens, Greece, 7–10 October 2018; pp. 400-404. [DOI: https://dx.doi.org/10.1109/ICIP.2017.8296311]

99. Banerjee, R.H.; Rajagopal, A.K.; Garg, V.; Borar, S. System for Deduplication of Machine Generated Designs from Fashion Catalog. Trends and Advances in Information Systems and Technologies: Volume 36; Springer: Cham, Switzerland, 2018; Volume 747, [DOI: https://dx.doi.org/10.1007/978-3-319-77700-9_6]

100. Vandecasteele, F.; Vandenbroucke, K.; Schuurman, D.; Verstockt, S. Spott: On-the-Spot e-Commerce for Television Using Deep Learning-Based Video Analysis Techniques. ACM Trans. Multimed. Comput. Commun. Appl.; 2017; 13, pp. 1-16. [DOI: https://dx.doi.org/10.1145/3092834]

101. Cychnerski, J.; Brzeski, A.; Boguszewski, A.; Marmołowski, M.; Trojanowicz, M. Clothes detection and classification using convolutional neural networks. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA; Limassol, Cyprus, 12–15 September 2017; pp. 1-8. [DOI: https://dx.doi.org/10.1109/ETFA.2017.8247638]

102. Yu, L.; Simo-Serra, E.; Moreno-Noguer, F.; Rubio, A. Multi-modal Embedding for Main Product Detection in Fashion. Proceedings of the IEEE International Conference on Computer Vision Workshops, ICCVW 2017; Venice, Italy, 22–29 October 2017; pp. 2236-2242. [DOI: https://dx.doi.org/10.1109/ICCVW.2017.261]

103. Ji, X.; Wang, W.; Zhang, M.; Yang, Y. Cross-Domain Image Retrieval with Attention Modeling. Proceedings of the 2017 ACM Multimendia Conference; Mountain View, CA, USA, 23–27 October 2017; pp. 1654-1662. [DOI: https://dx.doi.org/10.1145/3123266.3123429]

104. Corbiere, C.; Ben-Younes, H.; Rame, A.; Ollion, C. Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction. Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017; Venice, Italy, 22–29 October 2017; pp. 2268-2274. [DOI: https://dx.doi.org/10.1109/ICCVW.2017.266]

105. Schindler, A.; Lidy, T.; Karner, S.; Hecker, M. Fashion and apparel classification using convolutional neural networks. arXiv; 2018; arXiv: 1811.04374

106. Shamoi, P.; Inoue, A.; Kawanaka, H. Fuzzy Model for Human Color Perception and Its Application in E-Commerce. Int. J. Uncertain. Fuzziness-Knowl.-Based Syst.; 2016; 24, pp. 47-70. [DOI: https://dx.doi.org/10.1142/S0218488516400109]

107. Nogueira, K.; Veloso, A.A.; dos Santos, J.A. Pointwise and pairwise clothing annotation: Combining features from social media. Multimed Tools Appl.; 2016; 75, pp. 4083-4113. [DOI: https://dx.doi.org/10.1007/s11042-015-3087-2]

108. Hara, K.; Jagadeesh, V.; Piramuthu, R. Fashion apparel detection: The role of deep convolutional neural network and pose-dependent priors. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016; Lake Placid, NY, USA, 7–10 March 2016; [DOI: https://dx.doi.org/10.1109/WACV.2016.7477611]

109. Zhao, B.; Wu, X.; Peng, Q.; Yan, S. Clothing Cosegmentation for Shopping Images with Cluttered Background. IEEE Trans. Multimed.; 2016; 18, pp. 1111-1123. [DOI: https://dx.doi.org/10.1109/TMM.2016.2537783]

110. Huo, P.; Wang, Y.; Liu, Q. A Part-Based and Feature Fusion Method for Clothing Classification. Advances in Multimedia Information Processing—PCM 2016; Chen, E.; Gong, Y.; Tie, Y. Lecture Notes in Computer Science Springer: Cham, Switzerland, 2016; Volume 9916, [DOI: https://dx.doi.org/10.1007/978-3-319-48890-5_23]

111. Vecasteele, F.; Vervaeke, J.; Versmissen, B.; De Wachter, M.; Verstockt, S. Spatio-temporal wardrobe generation of actors’ clothing in video content. Proceedings of the International Conference on Human-Computer Interaction; Paris, France, 14–16 September 2016; Volume 9733, pp. 448-459. [DOI: https://dx.doi.org/10.1007/978-3-319-39513-5_42]

112. Cao, C.; Du, Y.; Ai, H. Bag detection and retrieval in street shots. Proceedings of the 22nd International Conference on MultiMedia Modeling, MMM 2016; Miami, FL, USA, 4–6 January 2016; Volume 9516, pp. 780-792. [DOI: https://dx.doi.org/10.1007/978-3-319-27671-7_65]

113. Shamoi, P.; Inoue, A.; Kawanaka, H. Deep color semantics for E-commerce content-based image retrieval. Proceedings of the CEUR Workshop Proceedings, IJCAI; Buenos Aires, Argentina, 25–31 July 2015; Volume 1424.

114. Rothe, R.; Ristin, M.; Dantone, M.; Van Gool, L. Discriminative learning of apparel features. Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015; Tokyo, Japan, 18–22 May 2015; pp. 5-9. [DOI: https://dx.doi.org/10.1109/MVA.2015.7153120]

115. Czapiewski, P.; Forczmański, P.; Frejlichowski, D.; Hofman, R. Clustering-Based Retrieval of Similar Outfits Based on Clothes Visual Characteristics. Image Processing & Communications Challenges 6; Springer: Cham, Switzerland, 2015; Volume 313, [DOI: https://dx.doi.org/10.1007/978-3-319-10662-5_4]

116. Nogueira, K.; Veloso, A.A.; Dos Santos, J.A. Learning to annotate clothes in everyday photos: Multi-modal, multi-label, multi-instance approach. Proceedings of the 27th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2014; Rio de Janeiro, Brazil, 26–30 August 2014; pp. 327-334. [DOI: https://dx.doi.org/10.1109/SIBGRAPI.2014.37]

117. Bhardwaj, A.; Sarma, A.D.; Di, W.; Hamid, R.; Piramuthu, R.; Sundaresan, N. Palette power: Enabling visual search through colors. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol Part F1288; Chicago, IL, USA, 11–14 August 2013; pp. 1321-1329. [DOI: https://dx.doi.org/10.1145/2487575.2488201]

118. Nodari, A.; Gallo, I.; Vanetti, M.; Albertini, S. Color and texture indexing using an object segmentation approach. Eng. Intell. Syst.; 2012; 20, pp. 47-57.

119. Zhu, X.; Huang, J.; Zhou, Q. Apparel image matting and applications in e-commerce. Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2011; Kandy, Sri Lanka, 20–22 August 2011; Volume 2, pp. 278-282. [DOI: https://dx.doi.org/10.1109/ITAIC.2011.6030330]

120. Spence, A.; Robb, M.; Timmins, M.; Chantler, M. Real-time per-pixel rendering of textiles for virtual textile catalogues. Int. J. Cloth. Sci. Technol.; 2004; 16, pp. 51-62. [DOI: https://dx.doi.org/10.1108/09556220410520351]

121. Chakraborty, S.; Hoque, M.S.; Jeem, N.R.; Biswas, M.C.; Bardhan, D.; Lobaton, E. Fashion Recommendation Systems, Models and Methods: A Review. Informatics; 2021; 8, 49. [DOI: https://dx.doi.org/10.3390/informatics8030049]

122. Ding, Y.; Ma, Y.; Wong, W.K.; Chua, T.-S. Leveraging Two Types of Global Graph for Sequential Fashion Recommendation. Proceedings of the 2021 International Conference on Multimedia Retrieval; Tokyo, Japan, 22–24 March 2021; pp. 73-81. [DOI: https://dx.doi.org/10.1145/3460426.3463638]

123. Koshy, R.; Gharat, A.; Wagh, T.; Sonawane, S. A complexion based outfit color recommender using neural networks. Proceedings of the 1st International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021; Bhilai, India, 19–20 February 2021; [DOI: https://dx.doi.org/10.1109/ICAECT49130.2021.9392418]

124. Bettaney, E.M.; Hardwick, S.R.; Zisimopoulos, O.; Chamberlain, B.P. Fashion outfit generation for e-commerce. Machine Learning and Knowledge Discovery in Databases; Applied Data Science and Demo Track Springer: Cham, Switzerland, 2021; Volume 12461, [DOI: https://dx.doi.org/10.1007/978-3-030-67670-4_21]

125. Addagarla, S.K.; Amalanathan, A. Probabilistic unsupervised machine learning approach for a similar image recommender system for E-commerce. Symmetry; 2020; 12, 1783. [DOI: https://dx.doi.org/10.3390/sym12111783]

126. Laenen, K.; Moens, M.-F. A Comparative Study of Outfit Recommendation Methods with a Focus on Attention-based Fusion. Inf. Process. Manag.; 2020; 57, 102316. [DOI: https://dx.doi.org/10.1016/j.ipm.2020.102316]

127. Deldjoo, Y.; Schedl, M.; Cremonesi, P.; Pasi, G. Recommender Systems Leveraging Multimedia Content. ACM Comput. Surv.; 2020; 53, pp. 1-38. [DOI: https://dx.doi.org/10.1145/3407190]

128. Gokturk, M.; Kabakli, B.; Yogurtcuglu, H. A Garment Suggestion Engine Based on Visual Similarity. Proceedings of the 28th Signal Processing and Communications Applications Conference (SIU); Gaziantep, Turkey, 5–7 October 2020; [DOI: https://dx.doi.org/10.1109/SIU49456.2020.9302291]

129. Fincato, M.; Cornia, M.L.; Cesari, F.; Cucchiara, R. Transform, Warp, and Dress: A New Transformation-guided Model for Virtual Try-on. ACM Trans. Multimed. Comput. Commun. Appl.; 2022; 18, pp. 1-24. [DOI: https://dx.doi.org/10.1145/3491226]

130. Li, J.; Xia, S.; West, A.J.; Istook, C.L. Fashionable sportswear working as a body measurement collecting tool. Int. J. Cloth. Sci. Technol.; 2022; [DOI: https://dx.doi.org/10.1108/IJCST-07-2021-0090]

131. Wen, C.-H.; Cheng, C.-C.; Shih, Y.-C. Artificial intelligence technologies for more flexible recommendation in uniforms. Data Technol. Appl.; 2022; [DOI: https://dx.doi.org/10.1108/DTA-09-2021-0230]

132. Wittmann, J.; Herl, G.; Hiller, J. Generation of a 3D model of the inside volume of shoes for e-commerce applications using industrial x-ray computed tomography. Eng. Res. Express; 2021; 3, 045058. [DOI: https://dx.doi.org/10.1088/2631-8695/ac43c8]

133. Wei, Z. Optimizing 3D Virtual Fitting Mirror Using Augmented Reality Technology under Internet Plus. Proceedings of the 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2022; Changchun, China, 25–27 February 2022; pp. 486-489. [DOI: https://dx.doi.org/10.1109/EEBDA53927.2022.9745006]

134. Fele, B.; Lampe, A.; Peer, P.; Struc, V. C-VTON: Context-Driven Image-Based Virtual Try-On Network. Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022; Waikoloa, HI, USA, 4–8 January 2022; pp. 2203-2212. [DOI: https://dx.doi.org/10.1109/WACV51458.2022.00226]

135. Majithia, S.; Parameswaran, S.N.; Babar, S.; Garg, V.; Srivastava, A.; Sharma, A. Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems. Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022; Waikoloa, HI, USA, 4–8 January 2022; pp. 1411-1421. [DOI: https://dx.doi.org/10.1109/WACV51458.2022.00148]

136. Foysal, K.H.; Chang, H.J.; Bruess, F.; Chong, J.W. Body size measurement using a smartphone. Electron; 2021; 10, 1338. [DOI: https://dx.doi.org/10.3390/electronics10111338]

137. Foysal, K.H.; Chang, H.J.; Bruess, F.; Chong, J.W. Smartfit: Smartphone application for garment fit detection. Electronics; 2021; 10, 97. [DOI: https://dx.doi.org/10.3390/electronics10010097]

138. Vladimirov, I.; Nikolova, D.; Terneva, Z. Overview of Methods for 3D Reconstruction of Human Models with Applications in Fashion E-Commerce. Proceedings of the 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies; Sozopol, Bulgaria, 16–18 June 2021; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2021; pp. 19-22. [DOI: https://dx.doi.org/10.1109/ICEST52640.2021.9483564]

139. Lai, H.; Lu, M. Dynamic VR Display System of Digital Garment Display Design Based on 5G Virtual Reality Technology. Big Data Analytics for Cyber-Physical System in Smart City; Atiquzzaman, M.; Yen, N.; Xu, Z. BDCPS 2020, Advances in Intelligent Systems and Computing Springer: Singapore, 2021; Volume 1303, [DOI: https://dx.doi.org/10.1007/978-981-33-4572-0_171]

140. Roy, D.; Santra, S.; Chanda, B. Incorporating Human Body Shape Guidance for Cloth Warping in Model to Person Virtual Try-on Problems. Proceedings of the 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ); Wellington, New Zealand, 25–27 November 2020; [DOI: https://dx.doi.org/10.1109/IVCNZ51579.2020.9290603]

141. Sanzam, S.; Das, S.G.; Sifat-Ul-Alam Jubair, M.I.; Ahmed, M.F. Image-to-Image Attire Transfer for Virtual Trial Room. Proceedings of the 23rd International Conference on Computer and Information Technology, Dhaka, Bangladesh, 19–21 December 2020; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2020; [DOI: https://dx.doi.org/10.1109/ICCIT51783.2020.9392671]

142. Sheth, M.; Srivastava, N. Predicting Body Size Using Mirror Selfies. Proceedings of the 11th International Conference on Intelligent Human Computer Interaction, IHCI 2019; Allahabad, India, 12–14 December 2020; Volume 11886, pp. 151-162. [DOI: https://dx.doi.org/10.1007/978-3-030-44689-5_14]

143. Moroz, M. Tendency to Use the Virtual Fitting Room in Generation Y—Results of Qualitative Study. Found Manag.; 2019; 11, pp. 239-254. [DOI: https://dx.doi.org/10.2478/fman-2019-0020]

144. Chou, C.-T.; Lee, C.-H.; Zhang, K.; Lee, H.-C.; Hsu, W.H. PIVTONS: Pose Invariant Virtual Try-On Shoe with Conditional Image Completion. Proceedings of the 14th Asian Conference on Computer Vision; Perth, Australia, 2–6 December 2019; Volume 11366, pp. 654-668. [DOI: https://dx.doi.org/10.1007/978-3-030-20876-9_41]

145. Karessli, N.; Guigoures, R.; Shirvany, R. SizeNet: Weakly supervised learning of visual size and fit in fashion images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Long Beach, CA, USA, 16–20 June 2019; pp. 335-343. [DOI: https://dx.doi.org/10.1109/CVPRW.2019.00046]

146. Guo, Z.; Zhang, D.; Li, J.; Lin, S.; Feng, S.; Xiao, Y. The design of a remote fitting system for garment e-commerce. J. Text Inst.; 2019; 110, pp. 243-253. [DOI: https://dx.doi.org/10.1080/00405000.2018.1476118]

147. Zagade, N.; Bhondave, A.; Asawa, R.; Raut, A.; Julme, B.C. Implementation of Virtual Trial Room for Shopping Websites Using Image Processing. Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB); Pandian, D.; Fernando, X.; Baig, Z.; Shi, F. ISMAC 2018, Lecture Notes in Computational Vision and Biomechanics Springer: Cham, Switzerland, 2019; Volume 30, [DOI: https://dx.doi.org/10.1007/978-3-030-00665-5_65]

148. Chandra, R.N.; Febriyan, F.; Rochadiani, T.H. Single camera body tracking for virtual fitting room application. Proceedings of the 10th International Conference on Computer and Automation Engineering. Association for Computing Machinery; Brisbane, Australia, 24–26 February 2018; pp. 17-21. [DOI: https://dx.doi.org/10.1145/3192975.3192991]

149. Liu, K.; Zeng, X.; Bruniaux, P.; Wang, J.; Kamalha, E.; Tao, X. Fit evaluation of virtual garment try-on by learning from digital pressure data. Knowl.-Based Syst.; 2017; 133, pp. 174-182. [DOI: https://dx.doi.org/10.1016/j.knosys.2017.07.007]

150. Wang, M.; Yu, C.; Fang, F. Consumer awareness and function requirement of three-dimensional virtual fitting. Wool Text J.; 2017; 45, pp. 78-83. [DOI: https://dx.doi.org/10.19333/j.mfkj.2016120261106]

151. Li, R.; Zhou, Y.; Zhu, S.; Mok, P.Y. Intelligent clothing size and fit recommendations based on human model customisation technology. Comput. Sci. Res. Notes; 2017; 2702, pp. 25-32.

152. Wang, Q.; Jagadeesh, V.; Ressler, B.; Piramuthu, R. Im2Fit: Fast 3d model fitting and anthropometrics using single consumer depth camera and synthetic data. Proceedings of the IS and T International Symposium on Electronic Imaging Science and Technology; San Francisco, CA, USA, 14–18 February 2016; [DOI: https://dx.doi.org/10.2352/ISSN.2470-1173.2016.21.3DIPM-045]

153. Bozkir, A.S.; Aydos, M. LogoSENSE: A companion HOG based logo detection scheme for phishing web page and E-mail brand recognition. Comput. Secur.; 2020; 95, 101855. [DOI: https://dx.doi.org/10.1016/j.cose.2020.101855]

154. Lang, Y.; He, Y.; Yang, F.; Dong, J.; Xue, H. Which is plagiarism: Fashion Image Retrieval based on Regional Representation for Design Protection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society; Seattle, WA, USA, 13–19 June 2020; pp. 2592-2601. [DOI: https://dx.doi.org/10.1109/CVPR42600.2020.00267]

155. Surya, S.; Setlur, A.; Biswas, A.; Negi, S. ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, WACV 2020; Snowmass Village, CO, USA, 1–5 March 2020; pp. 1189-1197. [DOI: https://dx.doi.org/10.1109/WACV45572.2020.9093459]

156. Magalhaes, A.R. The Trinity of Luxury Fashion Recommendations: Data, Experts and Experimentation. Proceedings of the13th ACM Conference on Recommender Systems; Copenhagen, Denmark, 16–20 September 2019; 522. [DOI: https://dx.doi.org/10.1145/3298689.3346978]

157. Zhou, Z.; Di, X.; Zhou, W.; Zhang, L. Fashion sensitive clothing recommendation using hierarchical collocation model. Proceedings of the 2018 ACM Multimedia Conference; Seoul, Republic of Korea, 22–26 October 2018; pp. 1119-1127. [DOI: https://dx.doi.org/10.1145/3240508.3240596]

158. Sikdar, S.; Sachdeva, R.; Wachs, J.; Lemmerich, F.; Strohmaier, M. The Effects of Gender Signals and Performance in Online Product Reviews. Front. Big Data; 2022; 4, 118. [DOI: https://dx.doi.org/10.3389/fdata.2021.771404]

159. Wang, S.; Qiu, J. A deep neural network model for fashion collocation recommendation using side information in e-commerce. Appl. Soft Comput.; 2021; 110, 107753. [DOI: https://dx.doi.org/10.1016/j.asoc.2021.107753]

160. Hananto, V.R.; Kim, S.; Kovacs, M.; Serdult, U.; Kryssanov, V. A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews. Proceedings of the 6th International Conference on Business and Industrial Research; Bangkok, Thailand, 20–21 May 2021; pp. 153-158. [DOI: https://dx.doi.org/10.1109/ICBIR52339.2021.9465830]

161. Hajjar, K.; Lasserre, J.; Zhao, A.; Shirvany, R. Attention Gets You the Right Size and Fit in Fashion. Recommender Systems in Fashion and Retail; Dokoohaki, N.; Jaradat, S.; Corona Pampín, H.J.; Shirvany, R. Lecture Notes in Electrical Engineering Springer: Cham, Switzerland, 2021; Volume 734, [DOI: https://dx.doi.org/10.1007/978-3-030-66103-8_5]

162. Morotti, E.; Donatiello, L.; Marfia, G. Fostering fashion retail experiences through virtual reality and voice assistants. Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Workshops (VRW 2020); Atlanta, GA, USA, 22–26 March 2020; pp. 338-342. [DOI: https://dx.doi.org/10.1109/VRW50115.2020.00074]

163. Li, W.; Xu, B. Aspect-Based Fashion Recommendation with Attention Mechanism. IEEE Access; 2020; 8, pp. 141814-141823. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3013639]

164. Sapna,; Chakraborty, R.; Vats, K.; Baradia, K.; Khan, T.; Sarkar, S.; Roychowdhury, S. Recommendence and fashionsence online fashion advisor for offline experience. Proceedings of the ACM International Conference Proceeding Series; Goa, India, 11–13 January 2019; pp. 256-259. [DOI: https://dx.doi.org/10.1145/3297001.3297035]

165. Cahya, R.A.; Bachtiar, F.A. Weakening Feature Independence of Naïve Bayes Using Feature Weighting and Selection on Imbalanced Customer Review Data. Proceedings of the 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019; Jogjakarta, Indonesia, 23–24 October 2019; pp. 182-187. [DOI: https://dx.doi.org/10.1109/ICSITech46713.2019.8987566]

166. Yang, X.; Ma, Y.; Liao, L.; Wang, M.; Chua, T.-S. TransNFCM: Translation-based neural fashion compatibility modeling. Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019; Honolulu, HI, USA, 28–29 January 2019; pp. 403-410.

167. Munigala, V.; Mishra, A.; Tamilselvam, S.G.; Khare, S.; Dasgupta, R.; Sankaran, A. PersuAIDE ! An Adaptive Persuasive Text Generation System for Fashion Domain. Proceedings of the Companion of the World Wide Web Conference, WWW 2018; San Francisco, CA, USA, 23–27 April 2018; pp. 335-342. [DOI: https://dx.doi.org/10.1145/3184558.3186345]

168. Luo, N.; Deng, H.; Zhao, L.; Liu, Y.; Wang, X.; Tan, Z. Multi-aspect feature based neural network model in detecting fake reviews. Proceedings of the 4th International Conference on Information Science and Control Engineering, ICISCE 2017; Changsha, China, 21–23 July 2017; pp. 475-479. [DOI: https://dx.doi.org/10.1109/ICISCE.2017.106]

169. Umer, M.; Ashraf, I.; Mehmood, A.; Kumari, S.; Ullah, S.; Sang Choi, G. Sentiment analysis of tweets using a unified convolutional neural network-long short-term memory network model. Comput. Intell.; 2021; 37, pp. 409-434. [DOI: https://dx.doi.org/10.1111/coin.12415]

170. Truong, N.; Nguyen, T.-T.; Luong, T.-L. Extracting Aspects in Product Reviews of Vietnamese e-Commerce Websites. IJCSNS Int. J. Comput. Sci. Netw. Secur.; 2020; 20, pp. 89-95. [DOI: https://dx.doi.org/10.22937/IJCSNS.2020.20.11.11]

171. Yang, J.; Li, L.; Lin, Y. Research on Online Word-of-mouth Sentiment Analysis and Attribute Extraction Based on Deep Learning. WHICEB 2020 Proceedings. 2020; Available online: https://aisel.aisnet.org/whiceb2020/32 (accessed on 21 September 2021).

172. Hamsagayathri, P.; Rajakumari, K. Machine learning algorithms to empower Indian women entrepreneur in E-commerce clothing. Proceedings of the 2020 International Conference on Computer Communication and Informatics. International Conference on Computer Communication and Informatics; Coimbatore, India, 22–24 January 2020.

173. Lin, X. Sentiment Analysis of E-commerce Customer Reviews Based on Natural Language Processing. Proceedings of the ACM International Conference Proceeding Series; Denton, TX, USA, 3–4 October 2020; pp. 32-36. [DOI: https://dx.doi.org/10.1145/3436286.3436293]

174. Androniceanu, A.; Georgescu, I.; Kinnunen, J. The Key Role of Social Media in Identifying Consumer Opinions for Building Sustainable Competitive Advantages. Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing; HCII 2020, Lecture Notes in Computer Science Springer: Cham, Switzerland, 2020; Volume 12195, [DOI: https://dx.doi.org/10.1007/978-3-030-49576-3_20]

175. Gopi, A.P.; Jyothi, R.N.S.; Narayana, V.L.; Sandeep, K.S. Classification of tweets data based on polarity using improved RBF kernel of SVM. Int. J. Inf. Technol.; 2020; 15, pp. 965-980. [DOI: https://dx.doi.org/10.1007/s41870-019-00409-4]

176. Novgorodov, S.; Guy, I.; Elad, G.; Radinsky, K. Generating product descriptions from user reviews. Proceedings of the World Wide Web Conference, WWW 2019; San Francisco, CA, USA, 13–17 May 2019; pp. 1354-1364. [DOI: https://dx.doi.org/10.1145/3308558.3313532]

177. See-To, E.W.K.; Ngai, E.W.T. Customer reviews for demand distribution and sales nowcasting: A big data approach. Ann. Oper. Res.; 2018; 270, pp. 415-431. [DOI: https://dx.doi.org/10.1007/s10479-016-2296-z]

178. Tao, R.; Luo, Y.; Liu, G. Sentiment analysis based on the domain dictionary: A case of analysing online apparel reviews. Int. J. Web. Eng. Technol.; 2018; 13, pp. 380-407. [DOI: https://dx.doi.org/10.1504/IJWET.2018.097563]

179. Elmurngi, E.I.; Gherbi, A. Unfair reviews detection on Amazon reviews using sentiment analysis with supervised learning techniques. J. Comput. Sci.; 2018; 14, pp. 714-726. [DOI: https://dx.doi.org/10.3844/jcssp.2018.714.726]

180. Zhang, D.; Xu, H.; Su, Z.; Xu, Y. Chinese comments sentiment classification based on word2vec and SVMperf. Expert Syst. Appl.; 2015; 42, pp. 1857-1863. [DOI: https://dx.doi.org/10.1016/j.eswa.2014.09.011]

181. Takatera, M.; Yoshida, R.; Peiffer, J.; Yamazaki, M.; Yashima, K.; Kim, K.; Miyatake, K. Fabric retrieval system for apparel e-commerce considering Kansei information. Int. J. Cloth. Sci. Technol.; 2020; 32, pp. 148-159. [DOI: https://dx.doi.org/10.1108/IJCST-03-2018-0035]

182. Yildirim, F.M.; Kaya, A.; Ozturk, S.N.; Kilinc, D. A Real-World Text Classification Application for an E-commerce Platform. Proceedings of the Innovations in Intelligent Systems and Applications Conference, ASYU 2019; Shenzhen, China, 31 October–2 November 2019; [DOI: https://dx.doi.org/10.1109/ASYU48272.2019.8946337]

183. Sawant, U.; Gabale, V. Product discovery from E-commerce listings via deep text parsing. Proceedings of the ACM International Conference Proceeding Series; Goa, India, 11–13 January 2018; pp. 98-107. [DOI: https://dx.doi.org/10.1145/3152494.3152503]

184. Aimé, X.; George, S.; Hornung, J. VetiVoc: A modular ontology for the fashion, textile and clothing domain. Appl. Ontol.; 2016; 11, pp. 1-28. [DOI: https://dx.doi.org/10.3233/AO-150149]

185. Kharfan, M.; Chan, V.W.K.; Firdolas Efendigil, T. A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches. Ann. Oper. Res.; 2020; 303, pp. 159-174. [DOI: https://dx.doi.org/10.1007/s10479-020-03666-w]

186. Shi, Y.; Wang, T.; Alwan, L.C. Analytics for Cross-Border E-Commerce: Inventory Risk Management of an Online Fashion Retailer. Decis. Sci.; 2020; 51, pp. 1347-1376. [DOI: https://dx.doi.org/10.1111/deci.12429]

187. Qi, Y.; Li, C.; Deng, H.; Cai, M.; Qi, Y.; Deng, Y. A deep neural framework for sales forecasting in e-commerce. Proceedings of the International Conference on Information and Knowledge Management; Beijing China, 3–7 November 2019; pp. 299-308. [DOI: https://dx.doi.org/10.1145/3357384.3357883]

188. Mohiuddin Babu, M.; Akter, S.; Rahman, M.; Billah, M.M.; Hack-Polay, D. The role of artificial intelligence in shaping the future of Agile fashion industry. Prod. Plan Control; 2022; pp. 1-5. [DOI: https://dx.doi.org/10.1080/09537287.2022.2060858]

189. Yim, S.T.; Son, J.C.; Lee, J. Spread of E-commerce, prices and inflation dynamics: Evidence from online price big data in Korea. J. Asian Econ.; 2022; 80, 101475. [DOI: https://dx.doi.org/10.1016/j.asieco.2022.101475]

190. Lyu, Y.; Lv(u), X. The Cutting-Edge Applications and Trends of Big Data and AI Technology in the Digitalization of the Fashion Industry. 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City; Springer: Singapore, 2022; Volume 102, [DOI: https://dx.doi.org/10.1007/978-981-16-7466-2_119]

191. Elia, S.; Giuffrida, M.; Mariani, M.M.; Bresciani, S. Resources and digital export: An RBV perspective on the role of digital technologies and capabilities in cross-border e-commerce. J. Bus Res.; 2021; 132, pp. 158-169. [DOI: https://dx.doi.org/10.1016/j.jbusres.2021.04.010]

192. Zhu, L.; Feng, J.; Sun, X.; Xue, P.; Meng, Q. Research on Comprehensive Budget Management of Clothing E-commerce Enterprises under the Background of Big Data: Take H Group as an Example. Proceedings of the International Conference on Communications, Information System and Computer Engineering, CISCE 2020; Online, 10 July 2020; pp. 178-182. [DOI: https://dx.doi.org/10.1109/CISCE50729.2020.00042]

193. Moorthi, K.; Srihari, K.; Karthik, S. Improving business process by predicting customer needs based on seasonal analysis: The role of big data in e-commerce. Int. J. Bus Excell.; 2020; 20, pp. 561-574. [DOI: https://dx.doi.org/10.1504/IJBEX.2020.106438]

194. Santalova, M.S.; Lesnikova, E.P.; Kustov, A.I.; Balahanova, D.K.; Nechaeva, S.N. Digital Technology in Retail: Reasons and Trends of Development. Ubiquitous Computing and the Internet of Things: Prerequisites for the Development of ICT; Studies in Computational Intelligence Springer: Cham, Switzerland, 2019; Volume 826, [DOI: https://dx.doi.org/10.1007/978-3-030-13397-9_110]

195. Giri, C.; Thomassey, S.; Balkow, J.; Zeng, X. Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression. Proceedings of the 2019 International Conference on Engineering, Science, and Industrial Applications, ICESI 2019; Semarang, Central Java, Indonesia, 18–19 July 2019; [DOI: https://dx.doi.org/10.1109/ICESI.2019.8863024]

196. Tseng, T. Developmental problems of current cross border e-commerce companies and countermeasures. Proceedings of the ICEB 2019 Procedings; Newcastle Upon Tyne, UK, 8–12 December 2019; pp. 567-569.

197. Gulhane, P.R.; Pradeep Kumar, T.S. TensorFlow based website click through Rate (CTR) prediction using heat maps. Proceedings of the International Conference on Recent Trends in Advanced Computing, ICRTAC-CPS 2018, VIT; Chennai, India, 10–11 September 2018; pp. 97-102. [DOI: https://dx.doi.org/10.1109/ICRTAC.2018.8679129]

198. Weichbrodt, L. Measuring Operational Quality of Recommendations. Proceedings of the 12th ACM Conference on Recommender Systems (RECSYS); Vancouver, BC, Canada, 2–7 October 2018; 485. [DOI: https://dx.doi.org/10.1145/3240323.3241725]

199. Suciu, M.-C.; Kolodziejak, A.; Năsulea, C.; Năsulea, D.-F.; Postma, E.J. The impact of big data on knowledge management systems in Romanian e-commerce retailers. Proceedings of the European Conference on Knowledge Management, ECKM; Padua, Italy, 6–7 September 2018; Volume 2, pp. 821-828.

200. Rambola, R.; Jatkar, M. An Effective Synchronization of ERP in Textile Industries. Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018; Coimbatore, India, 29–31 March 2018; pp. 969-973. [DOI: https://dx.doi.org/10.1109/ICECA.2018.8474686]

201. Lasserre, J.; Sheikh, A.-S.; Koriagin, E.; Bergman, U.; Vollgraf, R.; Shirvany, R. Meta-learning for size and fit recommendation in fashion. Proceedings of the SIAM International Conference on Data Mining, SDM 2020; Cincinnati, OH, USA, 7–9 May 2020; pp. 55-63. [DOI: https://dx.doi.org/10.1137/1.9781611976236.7]

202. Cardoso, A.; Daolio, F.; Vargas, S. Product characterisation towards personalisation: Learning attributes from unstructured data to recommend fashion products. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; London, UK, 19–23 August 2018; pp. 80-89. [DOI: https://dx.doi.org/10.1145/3219819.3219888]

203. Midha, N.; Singh, V. Classification of E-Commerce Products Using Reptree and k-Means Hybrid Approach. Big Data Analytics: Proceedings of CSI 2015; Springer: Singapore, 2018; Volume 654, [DOI: https://dx.doi.org/10.1007/978-981-10-6620-7_26]

204. De-Jia, Z. Strategic Research on the Fusion of Fashion Industry and New Media Industry in Wenzhou. Proceedings of the 6th International Conference on Social Science, Education and Humanities Research; Jinan, China, 18–19 October 2017; Volume 185, pp. 702-705.

205. Martino, G.; Fera, M.; Iannone, R.; Miranda, S. Proposal of a Multi-Method Decision Support System for the Fashion Retail Industry. Business Models and ICT Technologies for the Fashion Supply Chain; Rinaldi, R.; Bandinelli, R. IT4Fashion 2016, Lecture Notes in Electrical Engineering Springer: Cham, Switzerland, 2017; Volume 413, [DOI: https://dx.doi.org/10.1007/978-3-319-48511-9_16]

206. Banica, L.; Hagiu, A. Using Big Data Analytics to Improve Decision-Making in Apparel Supply Chains; Woodhead Publishing: Cambridge, UK, 2016; [DOI: https://dx.doi.org/10.1016/B978-0-08-100571-2.00004-X]

207. Jasek, P.; Vrana, L. Managerial Impacts of Different Computation Models for Customer Lifetime Value fon an e-coomerce Company. Proceedings of the 8th International Days of Statistics and Economics; Prague, Czech Republic, 11–13 September 2014; pp. 528-540.

208. Figueiredo, M.C.B. E-commerce: Forecasting demand for new products. Proceedings of the MCCSIS’08—IADIS Multi Conference on Computer Science and Information Systems, Proceedings e-Commerce 2008; Amsterdam, The Netherlands, 25–27 July 2008; pp. 102-112.

209. Pulkkinen, P.; Tiwari, N.; Kumar, A.; Jones, C. A Multi-Objective Rule Optimizer with an Application to Risk Management. Proceedings of the 17th IEEE International Conference on Machine Learning and Appilcations (ICMLA); Orlando, FL, USA, 17–20 December 2018; pp. 66-72. [DOI: https://dx.doi.org/10.1109/ICMLA.2018.00018]

210. Silva, E.S.; Bonetti, F. Digital humans in fashion: Will consumers interact?. J. Retail. Consum. Serv.; 2021; 60, 102430. [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102430]

211. Sheikh, A.S.; Guigourès, R.; Koriagin, E.; Ho, Y.K.; Shirvany, R.; Vollgraf, R.; Bergmann, U. A deep learning system for predicting size and fit in fashion e-commerce. Proceedings of the 13th ACM Conference on Recommender Systems; Copenhagen, Denmark, 16–20 September 2019; pp. 110-118. [DOI: https://dx.doi.org/10.1145/3298689.3347006]

212. Tupikovskaja-Omovie, Z.; Tyler, D. Eye tracking technology to audit google analytics: Analysing digital consumer shopping journey in fashion m-retail. Int. J. Inf. Manag.; 2021; 59, 102294. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2020.102294]

213. Niu, X.; Li, B.; Li, C.; Tan, J.; Xiao, R.; Deng, H. Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks. Proceedings of the 14th ACM International Conference on Web Search and Data Mining; Virtual Event, Israel, 8–12 March 2021; pp. 1038-1046. [DOI: https://dx.doi.org/10.1145/3437963.3441729]

214. Zavali, M.; Lacka, E.; de Smedt, J. Shopping Hard or Hardly Shopping: Revealing Consumer Segments Using Clickstream Data. IEEE Trans Eng Manag.; 2021; 70, pp. 1353-1364. [DOI: https://dx.doi.org/10.1109/TEM.2021.3070069]

215. Weingarten, J.; Spinler, S. Shortening Delivery Times by Predicting Customers’ Online Purchases: A Case Study in the Fashion Industry. Inf. Syst. Manag.; 2020; 38, pp. 287-308. [DOI: https://dx.doi.org/10.1080/10580530.2020.1814459]

216. Minatogawa, V.L.F.; Franco, M.M.V.; Rampasso, I.S.; Anholon, R.; Quadros, R.; Durán, O.; Batocchio, A. Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations. Sustainability; 2020; 12, 277. [DOI: https://dx.doi.org/10.3390/su12010277]

217. Mootha, S.; Sridhar, S.; Devi, M.S.K. A Stacking Ensemble of Multi Layer Perceptrons to Predict Online Shoppers’ Purchasing Intention. Proceedings of the 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020; Yogyakarta, Indonesia, 10 December 2020; pp. 721-726. [DOI: https://dx.doi.org/10.1109/ISRITI51436.2020.9315447]

218. Banerjee, D.; Rao, K.S.; Sural, S.; Ganguly, N. BOXREC: Recommending a Box of Preferred Outfits in Online Shopping. ACM Trans. Intell. Syst. Technol.; 2020; 11, pp. 1-28. [DOI: https://dx.doi.org/10.1145/3408890]

219. Pradeep, I.K.; Bhaskar, M.J.; Satyanarayana, B. Data science and deep learning applications in the e-commerce industry: A survey. Indian J. Comput. Sci. Eng.; 2020; 11, pp. 497-509. [DOI: https://dx.doi.org/10.21817/indjcse/2020/v11i5/201105135]

220. Jain, M.; Singh, S.; Chrasekaran, K.; Rathnamma, M.V.; Venkata Ramana, V. Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe. Proceedings of the 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020; Jaipur, India, 7–8 February 2020; pp. 12-17. [DOI: https://dx.doi.org/10.1109/ICETCE48199.2020.9091777]

221. Peixoto, V.; Peixoto, H.; Machado, J. Integrating a Data Mining Engine into Recommender Systems. Intelligent Data Engineering and Automated Learning—IDEAL 2020; Analide, C.; Novais, P.; Camacho, D.; Yin, H. IDEAL 2020. Lecture Notes in Computer Science Springer: Cham, Switzerland, 2020; Volume 12489, [DOI: https://dx.doi.org/10.1007/978-3-030-62362-3_19]

222. Arora, A.; Bansal, S.K.; Pal, C.; Aswani, R.; Dwivedi, Y. Measuring social media influencer index- insights from facebook, Twitter and Instagram. J. Retail. Consum. Serv.; 2019; 49, pp. 86-101. [DOI: https://dx.doi.org/10.1016/j.jretconser.2019.03.012]

223. Wernbacher, T.; Seewald, A.; Denk, N.; Pfeiffer, A.; Platzer, M.; Winter, T. Think!first: Inducing behavioural change through gamification, persuasive design principles and machine learning. Proceedings of the European Conference on Games-Based Learning; Odense, Denmark, 3–4 October 2019; pp. 793-801. [DOI: https://dx.doi.org/10.34190/GBL.19.156]

224. Seewald, A.K.; Wernbacher, T.; Pfeiffer, A.; Denk, N.; Platzer, M.; Berger, M.; Winter, T. Towards minimizing e-commerce returns for clothing. Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019); Prague, Czech Republic, 19–21 February 2019; Volume 2, pp. 801-808. [DOI: https://dx.doi.org/10.5220/0007568508010808]

225. Liu, Y.; Li, S. Research on marketing strategy of network womenswear brand based on big data statistics. Proceedings of the 34rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2019; Jinzhou, China, 6–8 June 2019; pp. 90-94. [DOI: https://dx.doi.org/10.1109/YAC.2019.8787653]

226. Gong, K.; Peng, Y.; Wang, Y.; Xu, M. Time series analysis for C2C conversion rate. Electron Commer. Res.; 2018; 18, pp. 763-789. [DOI: https://dx.doi.org/10.1007/s10660-017-9283-6]

227. Zou, M. Study on recommend model of online shopping for music and dance majors under the background of big data. Proceedings of the International Conference on Information Science and System, ICISS 2018; Bangalore, India, 17–19 December 2018; pp. 98-101. [DOI: https://dx.doi.org/10.1145/3209914.3226157]

228. Yan, C.; Huang, Y.; Zhang, Q.; Wan, Y. NSPD: An N-stage purchase decision model for E-commerce recommendation. Proceedings of the 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data; Macau, China, 23–25 July 2018; pp. 149-164. [DOI: https://dx.doi.org/10.1007/978-3-319-96890-2_13]

229. Chamberlain, B.P.; Cardoso, Â; Bryan Liu, C.H.; Pagliari, R.; Deisenroth, M.P. Customer lifetime value prediction using embeddings. Proceedings of the ACM International Conference Proceeding Series. Vol Part F1296; Chicago, IL, USA, 14–19 May 2017; pp. 1753-1762. [DOI: https://dx.doi.org/10.1145/3097983.3098123]

230. Tamhane, A.; Arora, S.; Warrier, D. Modeling contextual changes in user behaviour in fashion e-commerce. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining; Jeju, South Korea, 23–26 May 2017; Volume 10235, pp. 539-550. [DOI: https://dx.doi.org/10.1007/978-3-319-57529-2_42]

231. Yaǧci, A.M.; Aytekin, T.; Gürgen, F.S. Balanced random forest for imbalanced data streams. Proceedings of the 24th Signal Processing and Communication Application Conference; Zonguldak, Turkey, 16–19 May 2016; pp. 1065-1068. [DOI: https://dx.doi.org/10.1109/SIU.2016.7495927]

232. Schellong, D.; Kemper, J.; Brettel, M. Clickstream data as a source to uncover consumer shopping types in a large-scale online setting. Proceedings of the 24th European Conference on Information Systems, ECIS 2016; Istanbul, Turkey, 12–15 June 2016.

233. Golubtsov, N.; Galper, D.; Filchenkov, A. Active Adaptation of Expert-Based Suggestions in Ladieswear Recommender System LookBooksClub via Reinforcement Learning. Biologically Inspired Cognitive Architectures (BICA) for Young Scientists: Proceedings of the First International Early Research Career Enhancement School (FIERCES 2016); Springer: Cham, Switzerland, 2016; Volume 449, 449. [DOI: https://dx.doi.org/10.1007/978-3-319-32554-5_9]

234. Zhang, Y.; Chang, Q.; Wang, Y.; Yi, X. False reputation in online transactions: An empirical study. Proceedings of the 11th International Conference on Service Systems and Service Management, ICSSSM 2014; Beijing, China, 25–27 June 2014; [DOI: https://dx.doi.org/10.1109/ICSSSM.2014.6874108]

235. Nguyen, H.T.; Almenningen, T.; Havig, M.; Schistad, H.; Kofod-Petersen, A.; Langseth, H.; Ramampiaro, H. Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback. Mining Intelligence and Knowledge Exploration; Lecture Notes in Computer Science Prasath, R.; O’Reilly, P.; Kathirvalavakumar, T. Springer: Cham, Switzerland, 2014; Volume 8891, [DOI: https://dx.doi.org/10.1007/978-3-319-13817-6_6]

236. Nosu, K.; Ikeda, M. A preliminary analysis of item-selection behavior of Japanese female university students examining a 2D virtual fashion web site. IEEJ Trans. Electr. Electron. Eng.; 2014; 9, pp. 569-571. [DOI: https://dx.doi.org/10.1002/tee.22008]

237. Khodabandehlou, S.; Golpayegani, S.A.H.; Rahman, M.Z. An effective recommender system based on personality traits, demographics and behavior of customers in time context. Data Technol. Appl.; 2021; 55, pp. 149-174. [DOI: https://dx.doi.org/10.1108/DTA-04-2020-0094]

238. Hu, Z.-H.; Li, X.; Wei, C.; Zhou, H.-L. Examining collaborative filtering algorithms for clothing recommendation in e-commerce. Text Res. J.; 2019; 89, pp. 2821-2835. [DOI: https://dx.doi.org/10.1177/0040517518801200]

239. Jain, G.; Rakesh, S.; Kamalun Nabi, M.; Chaturvedi, K.R. Hyper-personalization—Fashion sustainability through digital clienteling. Res. J. Text Appar.; 2018; 22, pp. 320-334. [DOI: https://dx.doi.org/10.1108/RJTA-02-2018-0017]

240. Pal, G. An efficient system using implicit feedback and lifelong learning approach to improve recommendation. J. Supercomput.; 2022; 78, pp. 16394-16424. [DOI: https://dx.doi.org/10.1007/s11227-022-04484-6]

241. Bahuleyan, H.; Lasserre, J.; Lefakis, L.; Shirvany, R. Knowing When You Don’t Know in Online Fashion: An Uncertainty-Aware Size Recommendation Framework. Recommender Systems in Fashion and Retail: Proceedings of the Third Workshop at the Recommender Systems Conference; Springer: Cham, Switzerland, 2022; Volume 830, [DOI: https://dx.doi.org/10.1007/978-3-030-94016-4_3]

242. Budhiraja, A.; Sukhwani, M.; Aggarwal, M.; Shevade, S.; Sathyanarayana, G.; Tallamraju, R.B. Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users. Recommender Systems in Fashion and Retail: Proceedings of the Third Workshop at the Recommender Systems Conference; Springer: Cham, Switzerland, 2022; Volume 830, [DOI: https://dx.doi.org/10.1007/978-3-030-94016-4_1]

243. Modi, R.; Patel, R. Improving Collaborative Filtering Based Recommender System with Season and Style Features. Intelligent Sustainable Systems: Selected Papers of WorldS4 2021; Springer: Singapore, 2022; Volume 333, [DOI: https://dx.doi.org/10.1007/978-981-16-6309-3_44]

244. Ghanate, Y.; Mhaparle, A.; Yadav, S.; Mumbaikar, S. Smart Item Recommendation System for Offline Cloth Shop. ICDSMLA 2020: Proceedings of the 2nd International Conference on Data Science, Machine Learning and Applications 2022; Springer: Singapore, 2022; Volume 783, [DOI: https://dx.doi.org/10.1007/978-981-16-3690-5_55]

245. Eshel, Y.; Levi, O.; Roitman, H.; Nus, A. PreSizE: Predicting Size in E-Commerce using Transformers. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval; virtual event, 11–15 July 2021; pp. 255-264.

246. Kara, B.; Kalkan, H. Cloth Combine Estimation System Using Deep Learning. Proceedings of the 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020; Istanbul, Turkey, 15–17 October 2020; [DOI: https://dx.doi.org/10.1109/ASYU50717.2020.9259804]

247. da Cunha, D.S.; de Castro, L.N. Bioinspired Algorithms Applied to Association Rule Mining in Electronic Commerce Databases. Proceedings of the 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013; Ipojuca, Brazil, 8–11 September 2013; pp. 189-194. [DOI: https://dx.doi.org/10.1109/BRICS-CCI-CBIC.2013.40]

248. Nilashi, M.; Bagherifard, K.; Ibrahim, O.; Alizadeh, H.; Nojeem, L.A.; Roozegar, N. Collaborative filtering recommender systems. Res. J. Appl. Sci. Eng. Technol.; 2013; 5, pp. 4168-4182. [DOI: https://dx.doi.org/10.19026/rjaset.5.4644]

249. Lee, K.H.; Han, J.H.; Rhee, C.S. An agent system for products recommendation in electronic shopping mall. Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics; Orlando, FL, USA, 14–18 July 2002.

250. Sridevi, M.; Manikyaarun, N.; Sheshikala, M.; Sudarshan, E. Personalized fashion recommender system with image based neural networks. Proceedings of the IOP Conference Series: Materials Science and Engineering; Chennai, India, 16–17 September 2020; Volume 981, [DOI: https://dx.doi.org/10.1088/1757-899X/981/2/022073]

251. Malhotra, A.; Swaminathan, V.; Wu, G.; Schizas, I.D. Generative Networks for Synthesizing Human Videos in Text-Defined Outfits. Proceedings of the 21st International Workshop on Multimedia Signal Processing, MMSP 2019; Kuala Lumpur, Malaysia, 27–29 September 2019; [DOI: https://dx.doi.org/10.1109/MMSP.2019.8901706]

252. Chen, K.-T.; Luo, J. When fashion meets big data: Discriminative mining of best selling clothing features. Proceedings of the 26th International World Wide Web Conference 2017, WWW 2017 Companion; Perth, Australia, 3–7 April 2017; pp. 15-22. [DOI: https://dx.doi.org/10.1145/3041021.3054141]

253. Kalra, B.; Srivastava, K.; Prateek, M. Computer vision based personalized clothing assistance system: A proposed model. Proceedings of the 2nd International Conference on Next Generation Computing Technologies, NGCT 2016; Dehradun, India, 14–16 October 2016; pp. 341-346. [DOI: https://dx.doi.org/10.1109/NGCT.2016.7877438]

254. Gomes, J. Boosting Recommender Systems with Deep Learning. Proceedings of the 11th ACM Conference on Recommender Systems; Como, Italy, 27–31 August 2017; 344. [DOI: https://dx.doi.org/10.1145/3109859.3109926]

255. Fergurson, J.R. The paradox of diminishing returns: Measurement and metrics for valuation of B2C sales professionals. J. Mark. Channels.; 2020; 26, pp. 141-146. [DOI: https://dx.doi.org/10.1080/1046669X.2020.1747280]

256. Windsor.ai, Decathlon Sports Case Study—Data Integration and Machine-Learning Based Optimisations. 2020; Available online: https://windsor.ai/case-studies/decathlon/ (accessed on 19 April 2023).

257. Voda, A.I.; Radu, L.D. How can artificial intelligence respond to smart cities challenges?. Smart Cities: Issues and Challenges; Elsevier: Amsterdam, The Netherlands, 2019; pp. 199-216.

258. Bourg, L.; Chatzidimitris, T.; Chatzigiannakis, I.; Gavalas, D.; Giannakopoulou, K.; Kasapakis, V.; Konstantopoulos, C.; Kypriadis, D.; Pantziou, G.; Zaroliagis, C. Enhancing shopping experiences in smart retailing. J. Ambient. Intell. Humaniz. Comput.; 2021; [DOI: https://dx.doi.org/10.1007/s12652-020-02774-6]

259. Furini, M.; Mreoli, F.; Martoglia, R.; Montangero, M. IoT: Science fiction or real revolution?. Proceedings of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. Vol 195 LNICST; Springer: Berlin/Heidelberg, Germany, 2017; pp. 96-105. [DOI: https://dx.doi.org/10.1007/978-3-319-61949-1_11]

260. Saberi, Z.; Saberi, M.; Hussain, O.; Chang, E. Stackelberg model based game theory approach for assortment and selling price planning for small scale online retailers. Future Gener. Comput. Syst.; 2019; 100, pp. 1088-1102. [DOI: https://dx.doi.org/10.1016/j.future.2019.05.066]

261. Kong, L.; Liu, Z.; Wu, J. A systematic review of big data-based urban sustainability research: State-of-the-science and future directions. J. Clean Prod.; 2020; 273, 123142. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.123142]

262. Nozari, H.; Szmelter-Jarosz, A.; Ghahremani-Nahr, J. Analysis of the Challenges of Artificial Intelligence of Things (AIoT) for the Smart Supply Chain (Case Study: FMCG Industries). Sensors; 2022; 22, 2931. [DOI: https://dx.doi.org/10.3390/s22082931]

263. Davydova, T.; Turchenko, A.; Spivak, I.; Dubrovskaya, T. Customer engagement as the basis for technology decisions in a smart city. E3S Web of Conferences; EDP Sciences: Les Ulis, France, 2021; Volume 263, 04015.

264. Lynch, S.; Barnes, L. Omnichannel fashion retailing: Examining the customer decision-making journey. J. Fash Mark. Manag.; 2020; 24, pp. 471-493. [DOI: https://dx.doi.org/10.1108/JFMM-09-2019-0192]

265. Rao, P.H.N.; Vihari, N.S.; Jabeen, S.S. Reimagining the Fashion Retail Industry Through the Implications of COVID-19 in the Gulf Cooperation Council (GCC) Countries. FIIB Bus Rev.; 2021; 10, pp. 327-338. [DOI: https://dx.doi.org/10.1177/23197145211039580]

266. Silva, S.C.; Duarte, P.; Sundetova, A. Multichannel versus omnichannel: A price-segmented comparison from the fashion industry. Int. J. Retail. Distrib. Manag.; 2020; 48, pp. 417-430. [DOI: https://dx.doi.org/10.1108/IJRDM-07-2019-0232]

267. Ameen, N.; Tarhini, A.; Shah, M.; Madichie, N.O. Going with the flow: Smart shopping malls and omnichannel retailing. J. Serv. Mark.; 2020; 35, pp. 325-348. [DOI: https://dx.doi.org/10.1108/JSM-02-2020-0066]

268. Hayes, J.L.; Brinson, N.H.; Bott, G.J.; Moeller, C.M. The Influence of Consumer–Brand Relationship on the Personalized Advertising Privacy Calculus in Social Media. J. Interact. Mark.; 2021; 55, pp. 16-30. [DOI: https://dx.doi.org/10.1016/j.intmar.2021.01.001]

269. Liu, Z.; Qin, C.X.; Zhang, Y.J. Mining product competitiveness by fusing multisource online information. Decis. Support Syst.; 2021; 143, 113477. [DOI: https://dx.doi.org/10.1016/j.dss.2020.113477]

270. Lee, L.; Charles, V. The impact of consumers’ perceptions regarding the ethics of online retailers and promotional strategy on their repurchase intention. Int. J. Inf. Manag.; 2021; 57, 102264. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2020.102264]

271. Martínez, S.; Errasti, A.; Rudberg, M.; Mediavilla, M. Clothing industry: Main challenges in the supply chain management of value brand retailers. Lect. Notes Eng. Comput. Sci.; 2014; 3, pp. 69-76. [DOI: https://dx.doi.org/10.1007/978-3-319-14078-0_8]

272. Martinez, S.; Errasti, A.; Rudberg, M. Implementing Zara’s ‘ProntoModa’ paradigm at a value brand retailer: An empirical study. Proceedings of the 4th World P & OM Conference/19th Annual EurOMA Conference; Amsterdam, Holland, 2–4 July 2012.

273. Martínez, S.; Errasti, A.; Rudberg, M. Adapting Zara’s ‘Pronto Moda’ to a value brand retailer. Manag. Oper.; 2015; 26, pp. 723-737. [DOI: https://dx.doi.org/10.1080/09537287.2014.971526]

274. Kim, A. Fashion Recommendation with OpenAI’s GPT-2. Medium, 2019. Available online: https://towardsdatascience.com/fashion-recommendation-with-openais-gpt-2-3c4b8db4b4c4 (accessed on 19 April 2023).