Preference-Aware Mask for Session-Based Recommendation with Bidirectional Transformer
Yuanxing Zhang, Pengyu Zhao, Yushuo Guan, Lin Chen, Kaigui Bian, Lingyang Song, Bin Cui, Xiaoming Li
Abstract
User profiles are not always visible in E-commerce scenarios, in which case the recommender systems can only summarize users' preferences through sessions of historical records. However, the items in a session might be irrelevant to users' preferences or become the disturbances for modelling the users' portraits, and thus degrade the performance of the recommender systems. In this paper, we propose the preference-aware mask to capture user preferences over the items within the sessions, which adapts to the preference-irrelevant items within the sessions and provides explainable evidence for the recommendation. Evaluation over three real-world datasets verifies that MBTREC performs well on the new-item recommendation task, and outperforms several state-of-the-art recommender systems on the general metrics.