research-article

Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation

Authors:

Chengyuan Zhang,

Hongzhi YinAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 28, Pages 1 - 26

https://doi.org/10.1145/3466641

Published: 27 September 2021 Publication History

Abstract

With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.

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Index Terms

Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation
1. Networks
  1. Network properties
    1. Network reliability

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ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

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Publication History

Published: 27 September 2021

Accepted: 01 May 2021

Revised: 01 May 2021

Received: 01 October 2020

Published in TOIS Volume 40, Issue 2

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National Natural Science Foundation of China
Key Research and Technology Development Projects of Anhui Province
Science and Technology Plan of Hunan Province

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Chen RFan JWu MMa S(2025)Conditional diffusion model for recommender systemsNeural Networks10.1016/j.neunet.2025.107204185(107204)Online publication date: May-2025
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Zhang XGuo J(2024)A feature-enhanced knowledge graph neural network for machine learning method recommendationPeerJ Computer Science10.7717/peerj-cs.228410(e2284)Online publication date: 28-Aug-2024
https://doi.org/10.7717/peerj-cs.2284
Zhang SMeng XZhang Y(2024)Variational Type Graph Autoencoder for Denoising on Event RecommendationACM Transactions on Information Systems10.1145/370315643:1(1-27)Online publication date: 5-Nov-2024
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Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/3669906Online publication date: 29-May-2024
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Han JTang YTao QXia YZhang L(2024)Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/365397618:6(1-24)Online publication date: 26-Mar-2024
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Chen LCao JLiang WYe Q(2024)Geography-aware Heterogeneous Graph Contrastive Learning for Travel RecommendationACM Transactions on Spatial Algorithms and Systems10.1145/3641277Online publication date: 22-Jan-2024
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Cui CYao YZhang CMa HMa YRen ZZhang CKo J(2024)DGEKT: A Dual Graph Ensemble Learning Method for Knowledge TracingACM Transactions on Information Systems10.1145/363835042:3(1-24)Online publication date: 22-Jan-2024
https://doi.org/10.1145/3638350
Zhang HZhu YLi X(2024)Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of OneIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.339278246:11(7451-7462)Online publication date: Nov-2024
https://doi.org/10.1109/TPAMI.2024.3392782
Zou ZZhu XZhu QLiu YZhu L(2024)CREAMY: Cross-Modal Recipe Retrieval By Avoiding Matching ImperfectlyIEEE Access10.1109/ACCESS.2024.337015812(33283-33295)Online publication date: 2024
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Li YWang SXu SYin J(2024)Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identificationCAAI Transactions on Intelligence Technology10.1049/cit2.123019:3(544-556)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1049/cit2.12301
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