Disentangled representation learning for collaborative filtering based on hyperbolic geometry

Abstract

In the realm of recommender systems, the exploration of hyperbolic geometry-based embeddings for users and items has emerged as a promising avenue, particularly in the context of collaborative filtering through graph convolution networks. Despite the advancements in this domain, there are two significant questions have received limited attention: (i) Most hyperbolic geometry-based methods ignore the influence of different users’ intents on the preferences of historical behaviors; (ii) They usually learn high-dimensional embeddings akin to Euclidean geometry-based methods, without taking good advantage of the capacity of hyperbolic spaces. To tackle these limitations head-on, we propose a novel method called Disentangled Hyperbolic Collaborative Filtering (DHCF). DHCF learns multiple embeddings in different low-dimensional hyperbolic spaces, enabling the disentanglement of users’ intents and the separate modeling of user and item representations. Moreover, we design an intent-aware graph reconstruction module, which adaptively allocates intent-aware relation strengths to build a dynamic interaction graph. Additionally, this module reduces the risk of oversmoothing in low-dimension spaces, facilitating the stacking of multiple graph aggregation layers. To the best of our knowledge, our method achieves competitive performance compared to state-of-the-art approaches.