Category-guided multi-interest collaborative metric learning with representation uniformity constraints

Multi-interest collaborative metric learning has recently emerged as an effective approach to modeling the multifaceted interests of a user in recommender systems. However, two issues remain unexplored. (1) There is no explicit guidance for the matching of an item against multiple interest vectors of a user. (2) The desired property of item representations with respect to their categories is overlooked, resulting in that different categories of items are mixed up in the latent space. To overcome these issues, we devise a Category-guided Multi-interest Collaborative Metric Learning model (CMCML) with representation uniformity constraints. CMCML is designed as a novel category-guided Mixture-of-Experts (MoE) architecture, where the gating network leverages the item category to guide the matching of an item against multiple interest vectors of a user, encouraging items with the same category to approach the same interest vector. In addition, we design a user multi-interest uniformity loss and a category-aware item uniformity loss: The former aims to avoid representation degeneration by enlarging the difference among multiple interest vectors of the same user; the latter is tailored to push different categories of items apart in the latent space. Quantitative experiments on Ciao, Epinions and TaFeng demonstrate that our CMCML improves the value of NDCG@20 by 12.41%, 10.89% and 10.39% respectively, compared to other state-of-the-art collaborative metric learning methods. Further qualitative analyses reveal that our CMCML yields a better representation space where items from distinct categories are arranged in different regions with high density.