short-paper

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

Authors:

Yuhan XiaAuthors Info & Claims

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

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

Zhang YWang BZhang QZhu SMa Y(2025)Hypergraph User Embeddings and Session Contrastive Learning for POI RecommendationIEEE Access10.1109/ACCESS.2025.353139413(17983-17995)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3531394
Du HWang WBai LBai LLiang J(2025)A motif based hypergraph multi-level semantic encoding framework for social recommender systemsSignal Processing10.1016/j.sigpro.2024.109797230(109797)Online publication date: May-2025
https://doi.org/10.1016/j.sigpro.2024.109797
Wang YGuo CMa YFeng Q(2025)Recommendation feedback-based dynamic adaptive training for efficient social item recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125605262:COnline publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125605
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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

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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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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

Zhang YWang BZhang QZhu SMa Y(2025)Hypergraph User Embeddings and Session Contrastive Learning for POI RecommendationIEEE Access10.1109/ACCESS.2025.353139413(17983-17995)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3531394
Du HWang WBai LBai LLiang J(2025)A motif based hypergraph multi-level semantic encoding framework for social recommender systemsSignal Processing10.1016/j.sigpro.2024.109797230(109797)Online publication date: May-2025
https://doi.org/10.1016/j.sigpro.2024.109797
Wang YGuo CMa YFeng Q(2025)Recommendation feedback-based dynamic adaptive training for efficient social item recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125605262:COnline publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125605
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
Sun HTu ZSui DZhang BXu X(2024)A Federated Social Recommendation Approach with Enhanced Hypergraph Neural NetworkACM Transactions on Intelligent Systems and Technology10.1145/366593116:1(1-23)Online publication date: 30-Dec-2024
https://dl.acm.org/doi/10.1145/3665931
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
Zhang YZhang YZhao YDeng SYang Y(2024)Dual Variational Graph Reconstruction Learning for Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338689536:11(6002-6015)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3386895
Song YGu YLi TQi JLiu ZJensen CYu G(2024)CHGNN: A Semi-Supervised Contrastive Hypergraph Learning NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338064336:9(4515-4530)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3380643
Haghighi VSoltani BShabani NWu JZhang YYao LSheng QYang J(2024)TROPICAL: Transformer-Based Hypergraph Learning for Camouflaged Fraudster Detection2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00019(121-130)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00019
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