Session-based Recommendation with Local Invariance

Tianwen Chen, Raymond Chi-Wing Wong

Abstract: Session-based recommendation is a task to predict users' next actions given a sequence of previous actions in the same session. Existing methods either encode the previous actions in a strict order or completely ignore the order. However, sometimes the order of actions in a short sub-sequence, called the detailed order, may not be important, e.g., when a user is just comparing the same kind of products from different brands. Nevertheless, the high-level ordering information is still useful because the data is sequential in nature. Therefore, a good session-based recommender should pay different attention to the sequential information in different levels of granularity. To this end, we propose a novel model to automatically ignore the insignificant detailed ordering information in some sub-sessions, while keeping the high-level sequential information of the whole sessions. In the model, we first use a full self-attention layer with Gaussian weighting to extract features of sub-sessions, and then we apply a recurrent neural network to capture the high-level sequential information. Extensive experiments on two real-world datasets show that our method outperforms or matches the state-of-the-art methods.