A Bidirectional Representation Recommendation Based On Relevance Information Enhancement To Solve User Interest Drift
Nan Wang, Kun Li, Xuefeng Jiang, Jinbao Li · 2023
The phenomenon that user interests change dynamically over time is called user interest drift, which makes it difficult for recommendation systems to obtain users’ real interests. In order to better provide users with personalized services, it is very important to study recommendation methods that can be applied to user interest drift. However, the new interests of users are often very different from the interests that users have interacted with historically. It makes the correlation between the original user’s historical interaction and the user’s new interest very weak. Meanwhile, the information contained in user’s sessions is not enough to support the inference of the new interest, resulting in a serious decrease in model performance. To alleviate the above problem, we propose a bidirectional representation recommendation based on relevance information enhancement to solve user interest drift (RA-Bert). The model is based on Bert and uses bidirectional self-attention mechanism to represent user sessions. In this model, we design a relevance information enhancement module to adaptively capture the information associated with the user’s new interest from neighbors’ sessions. Then, we integrate the relevance information into user’s history sessions, which can effectively enhance the correlation between user’s history sessions and the new interest. Further, in order to effectively supplement the missing relevance information in users’ sessions, we design a new attention unit, which explicitly models the difference information between users and neighbors. Extensive experiments have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://github.com/LucasZen/paper_code.