Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation

Abstract

Sequential recommendation has attracted a lot of attention from both academia and industry. Since item embeddings directly affect the recommendation results, their learning process is very important. However, most existing sequential models may introduce bias when updating the item embeddings. For example, in a sequence where all items are endorsed by a same celebrity, the co-occurrence of two items only indicates their similarity in terms of endorser, and is independent of the other aspects such as category and color. The existing models often update the entire item as a whole or update different aspects of the item without distinction, which fails to capture the contributions of different aspects to the co-occurrence pattern. To overcome the above limitations, we propose aspect re-distribution (ARD) to focus on updating the aspects that are important for co-occurrence. Specifically, we represent an item using several aspect embeddings with the same initial importance. We then re-calculate the importance of each aspect according to the other items in the sequence. Finally, we aggregate these aspect embeddings into a single aspect-aware embedding according to their importance. The aspect-aware embedding can be provided as input to a successor sequential model. Updates of the aspect-aware embedding are passed back to the aspect embeddings based on their importance. Therefore, different from the existing models, our method pays more attention to updating the important aspects. In our experiments, we choose self-attention networks as the successor model. The experimental results on four real-world datasets indicate that our method achieves very promising performance in comparison with seven state-of-the-art models.