TCSR: Self-attention with time and category for session-based recommendation

Abstract

Session-based recommendation that uses sequence of items clicked by anonymous users to make recommendations has drawn the attention of many researchers, and a lot of approaches have been proposed. However, there are still problems that have not been well addressed: (1) Time information is either ignored or exploited with a fixed time span and granularity, which fails to understand the personalized interest transfer pattern of users with different clicking speeds; (2) Category information is either omitted or considered independent of the items, which defies the fact that the relationships between categories and items are helpful for the recommendation. To solve these problems, we propose a new session-based recommendation method, TCSR (self-attention with time and category for session-based recommendation). TCSR uses a non-linear normalized time embedding to perceive user interest transfer patterns on variable granularity and employs a heterogeneous SAN to make full use of both items and categories. Moreover, a cross-recommendation unit is adapted to adjust recommendations on the item and category sides. Extensive experiments on four real datasets show that TCSR significantly outperforms state-of-the-art approaches.