HSR: Hyperbolic Social Recommender
This addresses improving recommendation accuracy for users in social media platforms, though it appears incremental as it applies hyperbolic geometry to an existing problem.
The paper tackles social recommendation by proposing HSR, a framework that uses hyperbolic geometry to model user-item interactions and social relations, achieving better performance than Euclidean approaches and state-of-the-art methods in click-through rate prediction and top-K recommendation.
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.