Dual Part-pooling Attentive Networks for Session-based Recommendation

摘要

Session-based recommendation is proposed to predict user preferences in a short anonymous interaction session. There are two kinds of characteristic in the session: sequential dependencies and collective dependencies. Sequential dependencies mean that user-item interactions in the session are strictly ordered, while collective dependencies mean that multiple interactions with a flexible order jointly determine the user & rsquo;s next behaviors. However, existing methods only take one of the two characteristics into account, which leads to the fact that they can & rsquo;t model user behavior well. Furthermore, few methods emphasize the first interaction in the session, although user & rsquo;s initial intention can be reflected by it to a large extent. To this end, we propose a novel model, Dual Part-pooling Attentive Networks for session based Recommendation (DPAN4Rec), which is capable of capturing the sequential dependencies and collective dependencies of sessions simultaneously. By proposing a part-pooling attention mechanism, DPAN4Rec explores user & rsquo;s initial intention from first click and filters out noisy clicks in the session. Extensive experiments have been conducted on two benchmark e-commerce datasets, Yoochoose and Diginetica, and the experimental results show that DPAN4Rec outperforms state-of-the-art methods. Furthermore, our study demonstrates that it is necessary to consider both sequential dependencies and collective dependencies in session-based recommendation.(c) 2021 Elsevier B.V. All rights reserved.