Multi-source information contrastive learning collaborative augmented conversational recommender systems

Abstract

Conversational Recommender Systems (CRS) aim to provide high-quality items to users in fewer conversation rounds using natural language. Despite various attempts that have been made, there are still some problems: Previous CRS only learned item representations in a single knowledge graph and ignored item tags; information gaps exist in the same items from different knowledge graphs and information popularity both affect user preferences; system generated responses lack descriptiveness and diversity. To address these problems and fully utilize external knowledge, we propose a Multi-source Information Contrastive Learning Collaborative Augmented method ( $\mathbf{MCCA}$), which aims to mine the potential tag preferences of users in dialogues as well as enhance the accuracy of item representation and user preference modeling. Specifically, we utilize the obtained items and their tags to construct a new knowledge graph that incorporates movie tags. We design a Multi-source Item Fusion mechanism ( $\mathbf{MIF}$) to bridge the information gaps between items from different knowledge graphs and then utilize unsupervised contrastive learning to enhance the items’ representation capability after MIF. Additionally, a Multi-Tag Fusion mechanism ( $\mathbf{MTF}$) is designed to combine user-perceived information (i.e., tag popularity) and keywords obtained from reviews to co-enhance user preference representations through items and tags, and to incorporate fused item and tag features into the conversation module. Extensive experiments on two datasets show that MCCA significantly outperforms state-of-the-art methods. The source code will be available at https://github.com/lhy-cqut/MCCA.