推荐系统
计算机科学
双曲空间
社会化媒体
双曲线树
质量(理念)
情报检索
空格(标点符号)
双曲几何
万维网
数学
纯数学
认识论
操作系统
哲学
微分几何
作者
Anchen Li,Bo Yang,Farookh Khadeer Hussain,Huan Huo
标识
DOI:10.1016/j.ins.2021.11.040
摘要
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 the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, 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.
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