Sparse Sequential Recommendation with Interactions and Intentions Contrastive Learning

Sequential recommendation models user behavior dynamically based on historical interactions. However, data sparsity affects the performance of sequential recommendation consistently. Existing research mainly uses contrastive learning and auxiliary information to alleviate data sparsity. But existing contrastive learning methods only perform contrastive learning on interaction sequences. Auxiliary information enriches item representations, especially auxiliary intentions are widely adopted in the sequential recommendation. But the intent information is also sparse. And it is necessary to perform contrastive learning of the intention sequences corresponding to the interaction sequences. Previous research only uses contrastive learning and auxiliary intentions independently, which can result in general recommendation efficiency. How to combine contrastive learning and auxiliary intentions organically becomes more and more challenging. Therefore, we propose sparse sequential recommendation with interactions and intentions contrastive Learning, namely (ICL)-C-2, which solves the data sparsity problem by employing both contrastive learning and auxiliary intentions in the sequential recommendation. First, the intent representation is learned by selecting the intent information from a large amount of auxiliary information to compensate for the deficiency that the current sequential recommendation only focuses on recent items due to data sparsity. In this paper, the auxiliary intentions use the seller and classification information of items for user interaction, which can effectively capture users' long-term preferences. Second, for reasons of learning high-quality representations, we utilize a contrastive learning framework to extract self-supervised signals from raw user behavior sequences and intent sequences and optimize user representation models so as to improve sequential recommender systems. Finally, a multi-task training strategy is adopted to optimize recommendations jointly through parameter and structure sharing. Experiments are conducted on two sparse recommendation datasets to demonstrate the effectiveness of the proposed model in this paper.