Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems

• URL: http://arxiv.org/abs/2311.06323v1
• Date: Fri, 10 Nov 2023 12:11:36 GMT
• Title: Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
• Authors: Haojun Zhu, Vikram Kapoor, Priya Sharma
• Abstract summary: We try to review recent literature on graph neural network-based recommender systems. We explore the motivation behind incorporating graph neural networks into recommender systems.
• Score: 1.2277343096128712
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The Recommender system is a vital information service on today's Internet. Recently, graph neural networks have emerged as the leading approach for recommender systems. We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems. We also analyze challenges and open problems in graph construction, embedding propagation and aggregation, and computation efficiency. This guides us to better explore the future directions and developments in this domain.

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