文献类型：期刊文献

英文题名：Heterogeneous information fusion based graph collaborative filtering recommendation

作者：Mu, Ruihui[1];Zeng, Xiaoqin[2];Zhang, Jiying[1]

第一作者：穆瑞辉

通讯作者：Mu, RH[1]

机构：[1]Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang, Henan, Peoples R China;[2]Hohai Univ, Coll Comp & Informat, Nanjing, Jiangsu, Peoples R China

第一机构：新乡学院计算机与信息工程学院

通讯机构：[1]corresponding author), Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang, Henan, Peoples R China.|[1107118]新乡学院计算机与信息工程学院;[11071]新乡学院;

年份：2023

卷号：27

期号：6

起止页码：1595-1613

外文期刊名：INTELLIGENT DATA ANALYSIS

收录：;EI(收录号：20234915174473);Scopus(收录号：2-s2.0-85178609982);WOS:【SCI-EXPANDED(收录号:WOS:001111259700004)】；

语种：英文

外文关键词：Heterogeneous information; collaborative filtering; graph neural network; recommender systems

摘要：Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework.