Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation

Traditional recommender systems mainly aim to model inherent and long-term user preference, while dynamic user demands are also of great importance. Typically, a historical consumption will have impacts on the user demands for its relational items. For instance, users tend to buy complementary items together (iPhone and Airpods) but not substitutive items (Powerbeats and Airpods), although substitutes of the bought one still cater to his/her preference. To better model the effects of history sequence, previous studies introduce the semantics of item relations to capture user demands for recommendation. However, we argue that the temporal evolution of the effects caused by different relations cannot be neglected. In the example above, user demands for headphones can be promoted after a long period when a new one is needed.

To model dynamic meanings of an item in different sequence contexts, a novel method Chorus is proposed to take both item relations and corresponding temporal dynamics into consideration. Chorus aims to derive the embedding of target item in a knowledge-aware and time-aware way, where each item will get its basic representation and relation-related ones. Then, we devise temporal kernel functions to combine these representations dynamically, according to whether there are relational items in history sequence as well as the elapsed time. The enhanced target item embedding is flexible to work with various algorithms to calculate the ranking score and generate recommendations. According to extensive experiments in three real-world datasets, Chorus gains significant improvements compared to state-of-the-art baseline methods. Furthermore, the time-related parameters are highly interpretable and hence can strengthen the explainability of recommendation.