Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning

Abstract

Sequential recommendation predicts next items for users based on their historical interactions. Existing methods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences, especially for long-tail users. Therefore, a sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning was proposed. Firstly, semantic item similarity was introduced by leveraging relationships between entities in the knowledge graph to extract correlated items from original sequences. Secondly, two sequence augmentation operators were proposed based on different contrastive learning views, addressing the problem of insufficient training for long-tail user sequences by augmenting self-supervised signals. Finally, precise sequence recommendations were provided for long-tail users by utilizing the joint training of shared network parameters between contrastive self-supervised tasks and the recommendation task. Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users.

Keywords

• sequential recommendation;long-tail user;knowledge graph;contrastive learning