Hyperbolic Multi-Criteria Rating Recommendation

Multi-criteria (MC) ratings as auxiliary supervisory signals can improve the prediction accuracy of recommender systems. The existing MC methods learn the representations of users and items in Euclidean space to estimate the interaction probabilities. However, this modeling paradigm ignores two important aspects. Firstly, when embedding power-law distribution data and personalized MC preferences in Euclidean space, the model may produce suboptimal solutions due to the distortion of the hierarchical structure. Secondly, the inevitable noise in MC ratings may hinder the recommendation quality of the model. To address the above issues, we propose a novel framework called Hyperbolic Multi-Criteria Recommendation (HMCR), which aims to mine users' MC behavioral features on hyperbolic manifolds and mitigate the noise interference through knowledge transfer among the criteria. Specifically, we map the representations on each criterion view to a hyperbolic space with adjustable curvature based on the Lorentz model, which is used to capture the hierarchical structure of collective user behavior. The MC preferences of individual users are fused by calculating the hyperbolic attention among each criterion and the overall rating. Moreover, we design a self-supervised contrastive loss to suppress the negative impact of noise interactions on the model. The experimental results on four real-world datasets show that the HMCR significantly outperforms the existing baselines.