HSR: Hyperbolic Social Recommender

TL;DR

This paper introduces HSR, a novel social recommender system that leverages hyperbolic geometry to improve user and item representations, leading to better recommendation performance.

Contribution

The paper proposes a hyperbolic geometry-based framework for social recommendation, outperforming Euclidean models and state-of-the-art methods in key metrics.

Findings

HSR outperforms Euclidean-based recommenders.

HSR achieves higher click-through rates.

HSR improves top-K recommendation accuracy.

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.