Learning Dual-view User Representations for Enhanced Sequential Recommendation

Sequential recommendation (SR) aims to predict a user’s next interacted item given his/her historical interactions. Most existing sequential recommendation systems model user preferences only with item-level representations, where a user’s interaction sequence are often modeled with sequential or graph-based method to infer the user’s sequential interaction pattern. However, since a user’s preference factors may vary over time, the user modeling on item-level could hardly represent the user’s preference precisely and sufficiently, resulting in suboptimal recommendation performance. In addition, the recommendation results based on the item-level user representations lack the interpretability of preference factors. To address these problems, we propose a novel SR model with dual-view user representations in this paper, namely DUVRec, where a user’s preference is learned based on the representations of two distinct views, i.e., item view and factor view . Specifically, the item-view user representation is learned as the previous SR models to encode the user preference of item level, while the factor-view user representation is learned by an coarse-grained graph embedding method to explicitly represent the user in terms of preference factors. As a result, such dual-view user representations are more comprehensive than that in the previous SR models, leading to enhanced SR performance. Furthermore, we design a contrastive learning strategy to achieve mutual complementation between these two views. Our extensive experiments upon three benchmark datasets justify DUVRec’s superior performance over the state-of-the-art SR models, including the advantage of the dual-view contrastive learning. In addition, DUVRec’s capability of providing explanations on recommendation results is also demonstrated through some specific case studies.