Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems

Autor:	Zhu, Haojun, Kapoor, Vikram, Sharma, Priya
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.06323 Zobrazit plný text záznamu View this record from Arxiv