Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN

Abstract

Recent advances in sequence-aware approaches for session-based recommendation, such as those based on recurrent neural networks, highlight the importance of leveraging sequential information from a session while making recommendations. Further, a session based k-nearest-neighbors approach (SKNN) has proven to be a strong baseline for session-based recommendations. However, SKNN does not take into account the readily available sequential and temporal information from sessions. In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case. STAN takes into account the following factors for making recommendations: i) position of an item in the current session, ii) recency of a past session w.r.t. to the current session, and iii) position of a recommendable item in a neighboring session. The importance of above factors for a specific application can be adjusted via controllable decay factors. Despite being simple, intuitive and easy to implement, empirical evaluation on three real-world datasets shows that STAN significantly improves over SKNN, and is even comparable to the recently proposed state-of-the-art deep learning approaches. Our results suggest that STAN can be considered as a strong baseline for evaluating session-based recommendation algorithms in future.