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

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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References

[1]
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In The World Wide Web Conference (San Francisco, CA, USA) (WWW '19). Association for Computing Machinery, New York, NY, USA, 417--426. https://doi.org/10.1145/3308558.3313488
[2]
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019. Hypergraph Neural Networks. In Proceedings of the AAAI Conference on Artificial Intelligence. 3558--3565. https://doi.org/10.1609/aaai.v33i01.33013558
[3]
Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings.
[4]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 639--648. https://doi.org/10.1145/3397271.3401063
[5]
Mohsen Jamali and Martin Ester. 2010. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks. In Proceedings of the Fourth ACM Conference on Recommender Systems (Barcelona, Spain) (RecSys'10). Association for Computing Machinery, New York, NY, USA, 135--142. https://doi.org/10.1145/1864708.1864736
[6]
Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, and Yue Gao. 2020. Dual Channel Hypergraph Collaborative Filtering. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining (Virtual Event, CA, USA) (KDD '20). Association for Computing Machinery, New York, NY, USA, 2020--2029. https://doi.org/10.1145/3394486.3403253
[7]
Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, and Yue Gao. 2019. Dynamic Hypergraph Neural Networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 2635--2641. https://doi.org/10.24963/ijcai.2019/366
[8]
Yehuda Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Las Vegas, Nevada, USA) (KDD '08). Association for Computing Machinery, New York, NY, USA, 426--434. https://doi.org/10.1145/1401890.1401944
[9]
Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. SoRec: Social Recommendation Using Probabilistic Matrix Factorization. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (Napa Valley, California, USA) (CIKM '08). Association for Computing Machinery, New York, NY, USA, 931--940. https://doi.org/10.1145/1458082.1458205
[10]
Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender Systems with Social Regularization. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (Hong Kong, China) (WSDM '11). Association for Computing Machinery, New York, NY, USA, 287--296. https://doi.org/10.1145/1935826.1935877
[11]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI '09). AUAI Press, Arlington, Virginia, USA, 452--461.
[12]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 165--174. https://doi.org/10.1145/3331184.3331267
[13]
Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, and Meng Wang. 2020. DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation. IEEE Transactions on Knowledge and Data Engineering, 1--1. https://doi.org/10.1109/TKDE.2020.3048414
[14]
Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019. A Neural Influence Diffusion Model for Social Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 235--244. https://doi.org/10.1145/3331184.3331214
[15]
Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, and Meng Wang. 2019. SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. arXiv:1811.02815 [cs.IR]
[16]
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems (WWW'19). Association for Computing Machinery, New York, NY, USA, 2091--2102. https://doi.org/10.1145/3308558.3313442
[17]
Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen QuocViet Hung. 2021. Socially-Aware Self-Supervised Tri-Training for Recommendation. Association for Computing Machinery, New York, NY, USA, 2084--2092. https://doi.org/10.1145/3447548.3467340
[18]
Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. 2021. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW '21). Association for Computing Machinery, New York, NY, USA, 413--424. https://doi.org/10.1145/3442381.3449844
[19]
Wei Yu and Shijun Li. 2018. Recommender systems based on multiple social networks correlation. Future Gener. Comput. Syst. 87, 312--327. https://doi.org/10.1016/j.future.2018.04.079

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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
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2022

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    Author Tags

    1. homogeneity
    2. hypergraph convolution network
    3. multiple social recommendations

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    • (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
    • (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
    • (2024)Self-Supervised Hypergraph Learning for Knowledge-Aware Social RecommendationElectronics10.3390/electronics1307130613:7(1306)Online publication date: 31-Mar-2024
    • (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
    • (2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
    • (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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    • (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
    • (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
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