Context-Aware Collaborative Filtering Framework for Rating Prediction Based on Novel Similarity Estimation

Abstract

Recommender systems are rapidly transforming the digital world into intelligent information hubs. The valuable context information associated with the users’ prior transactions has played a vital role in determining the user preferences for items or rating prediction. It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades. This paper presents a novel Context Based Rating Prediction (CBRP) model with a unique similarity scoring estimation method. The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential users to forecast the item ratings. The context scoring strategy has an inherent capability to incorporate multiple conditional factors to filter down the most relevant recommendations. Compared with traditional similarity estimation methods, CBRP makes it possible for the full use of neighboring collaborators’ choice on various conditions. We conduct experiments on three publicly available datasets to evaluate our proposed method with random user-item pairs and got considerable improvement in prediction accuracy over the standard evaluation measures. Also, we evaluate prediction accuracy for every user-item pair in the system and the results show that our proposed framework has outperformed existing methods.