  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.
