Learning and Fusing Multiple User Interest Representations for Sequential Recommendation

Deep learning is known to be effective at automating the generation of representations, which has achieved great success by learning efficient representations from data, especially for user and item representations in sequential recommendation. However, most existing methods usually represent a user's interest by one independent representation vector, which is inadequate to capture user's diverse interests. We aim to fully characterize the diversity of user interests in this study to improve the recommendation performance. We propose a Multiple User Interest Representations (MUIR) model that learns and fuses user's interests from different aspects. To learn different levels of user interests, we specifically leverage two self-attention-based modules that better capture user's local and global interests respectively. By considering the information of the recent interacted items, we further design a gating module to balance the interests, which is capable of modeling how user interests evolve and interact in recent behavior sequence. Extensive experiments conducted on the real-world datasets demonstrate that our model, MUIR, outperforms existing state of-the-art methods significantly.