Towards Interest Drift-driven User Representation Learning in Sequential Recommendation

Sequential recommendation (SR) aims to infer users' future interests and suggest the next items for them. Most SR methods learn one single vector to represent a user's recent interests, i.e., a user representation. Despite their great success, most of them do not explicitly consider the phenomenon of users' interest drift when learning user representations. Moreover, interest drift presents two critical challenges for these SR methods: (1) how to explore the potential distributions of the users' varying interest drift levels; and (2) how to capture the interest drift-aware collaborative knowledge among the users. In this paper, we delve into the issue of interest drift in SR and propose a novel and generic framework, i.e., Interest Drift-driven User Representation Learning (IDURL), to enhance SR methods to tackle the above two challenges. Specifically, our IDURL contains an interest drift quantization (IDQ) module to enable a quantitative measurement of the interest drift. Moreover, a drift representation generation module models the users' latent varying levels of interest drift, and an interest drift-guided representation disentanglement module optimizes the distributions of the interest drift levels under the guidance of IDQ. Furthermore, an interest drift-aware representation alignment module helps to capture the interest drift-aware collaborative knowledge among users. Finally, the users' overall interest representations are obtained to calculate the preference scores on the candidate items. Extensive experiments on four public datasets show the effectiveness of our IDURL. The source code of our IDURL is available at: https://github.com/xiaolLIN/IDURL.