Hyperbolic Neural Collaborative Recommender

2021/04/15 by Anchen Li, Bo Yang, Li, Anchen +5
Computer Science · #Advanced Graph Neural Networks #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Information Retrieval (cs.IR) #Recommender Systems and Techniques

paper · pdf · doi:10.48550/arxiv.2104.07414

openalex publication_date 2021/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Matrix Factorization Techniques for Recommender Systems
• Multidimensional binary search trees used for associative searching
• Hyperbolic Attention Networks
• Hyperbolic Graph Attention Network
• Geom-GCN: Geometric Graph Convolutional Networks
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
• LightGCN

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