short-paper

DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations

Authors:

Yufei Tang, and

Yuhan XiaAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

Pages 2190 - 2194

https://doi.org/10.1145/3477495.3531828

Published: 07 July 2022 Publication History

Abstract

Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit pairwise relations to mine user preferences), ignoring the impact of multifaceted social relations on user preferences (i.e., high order complexity of user relations). Moreover, an observing fact is that similar items always have similar attractiveness when exposed to users, indicating a potential connection among the static attributes of items. Here, we advocate modeling the dual homogeneity from social relations and item connections by hypergraph convolution networks, named DH-HGCN, to obtain high-order correlations among users and items. Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.

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Cited By

Li MLi JYang LDing Q(2024)Self-Supervised Hypergraph Learning for Knowledge-Aware Social RecommendationElectronics10.3390/electronics1307130613:7(1306)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071306
Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
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
https://dl.acm.org/doi/10.1145/3653976
Show More Cited By

Index Terms

DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Publication History

Published: 07 July 2022

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Short-paper

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National Key Research and Development Program of China

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Li MLi JYang LDing Q(2024)Self-Supervised Hypergraph Learning for Knowledge-Aware Social RecommendationElectronics10.3390/electronics1307130613:7(1306)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071306
Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
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
https://dl.acm.org/doi/10.1145/3653976
Dai JLi QNong TBi QChu H(2024)Joint contrastive learning of structural and semantic for graph collaborative filteringNeurocomputing10.1016/j.neucom.2024.127547586(127547)Online publication date: Jun-2024
https://doi.org/10.1016/j.neucom.2024.127547
Yan YZhang CLi XZhang B(2024)A framework for stock selection via concept-oriented attention representation in hypergraph neural networkKnowledge-Based Systems10.1016/j.knosys.2023.111326284:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111326
Yang FPeng D(2024)A graph neural network with topic relation heterogeneous multi-level cross-item information for session-based recommendationInformation Systems10.1016/j.is.2024.102380123(102380)Online publication date: Jul-2024
https://doi.org/10.1016/j.is.2024.102380
Liu BLi DWang JWang ZLi BZeng C(2024)Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10368061:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103680
Wang HZhou WWen JQiao S(2024)Multiple hypergraph convolutional network social recommendation using dual contrastive learningData Mining and Knowledge Discovery10.1007/s10618-024-01021-2Online publication date: 24-Apr-2024
https://doi.org/10.1007/s10618-024-01021-2
Liu XMeng SLi QQi LXu XDou WZhang XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615063(1566-1575)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615063
Li SWang JZeng HLi JWei YLi X(2023)Dynamic Social Recommendation with High-Matching Inhomogeneous Relations2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394078(526-533)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394078
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