Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning

Abstract

Sequential recommendation (SR) provides personalized contents based on the user’s historical interactions. Previous SR methods focus on introducing temporal signals of interaction sequence into their sequence encoders without exploring the effect of temporal density information on user preferences. To bridge this gap, we propose the Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning (TDSRec). The specifics of our research mainly consist of two parts. First, we integrate temporal density information into sequential recommendation when capturing user preferences. In detail, through our proposed Temporal KDE Module, we map timestamps into temporal density vectors aiming at improving the recommendation performance. Second, we introduce contrastive learning into TDSRec to alleviate the lack of supervised learning signals as a result of sparse user-item interactions. In detail, instead of directly editing the raw sequence, we leverage two base sequence encoders to derive self-supervision signals. In this way, our approach can naturally eliminate the interference of hand-crafted data augmentation strategies on the raw sequence data. Through extensive experiments on five test datasets, we find that our proposed TDSRec outperforms state-of-the-art baselines. NDCG@10 has been improved by 5.1%, 2.9%, 2.1%, 1.9%, and 1.4% on MovieLens, Beauty, Video Games, CDs&Vinyl, and Movies&TV, respectively.