2021/04/15 by Anchen Li, Bo Yang, Li, Anchen +5 · 3 citations
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Artificial neural network #Collaborative filtering #Computer science #Construct (python library) #Convolutional neural network #Deep learning #Euclidean geometry #Exploit #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Information Retrieval (cs.IR) #Information retrieval #Mathematics #Recommender Systems and Techniques #Recommender system #Representation (politics) #Set (abstract data type) #cs.IR
paper · pdf · doi:10.48550/arxiv.2104.07414
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: substantial text overlap with arXiv:2102.09389
arxiv created 2021/04/15 · openalex publication_date 2021/04/15 · arxiv updated 2021/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relations among users/items for collaborative filtering (CF) tasks. HNCR contains two major phases: neighbor construction and recommendation framework. The first phase introduces a neighbor construction strategy to construct a semantic neighbor set for each user and item according to the user-item historical interaction. In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation. Via a series of extensive experiments, we show that HNCR outperforms its Euclidean counterpart and state-of-the-art baselines.