HSR: Hyperbolic Social Recommender

Abstract

With the prevalence of online social media, users' social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic spaces, HSR can learn high-quality user and item representations for better modeling user-item interaction and user-user social relations. Via a series of extensive experiments, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.

References in corpus (8)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Hierarchical structure and the prediction of missing links in networks
• Hyperbolic Geometry of Complex Networks
• Poincaré GloVe: Hyperbolic Word Embeddings
• Hyperbolic Attention Networks
• Scalable Hyperbolic Recommender Systems
• Hyperbolic Graph Attention Network
• Modelling High-Order Social Relations for Item Recommendation

Cited by in corpus (1)

• HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation