Abstract
Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only model pairwise node relationships and are not conducive to exploring higher-order connectivity, while hypergraph provides a natural way to model high-order relations between nodes. However, recent studies show that social recommendations still face the following challenges: 1) a majority of social recommendations ignore the impact of multifaceted social relationships on user preferences; 2) the item homogeneity is often neglected, mainly referring to items with similar static attributes have similar attractiveness when exposed to users that indicating hidden links between items; and 3) directly combining the representations learned from different independent views cannot fully exploit the potential connections between different views. To address these challenges, in this paper, we propose a novel method DH-HGCN++ for multiple social recommendations. Specifically, dual homogeneity (i.e., social homogeneity and item homogeneity) is introduced to mine the impact of diverse social relations on user preferences and enrich item representations. Hypergraph convolution networks with motifs are further exploited to model the high-order relations between nodes. Finally, cross-view contrastive learning is proposed as an auxiliary task to jointly optimize the DH-HGCN++. Real-world datasets are used to validate the effectiveness of the proposed model, where we use sentiment analysis to extract comment relations and employ the k-means clustering algorithm to construct the item-item correlation graph. Experiment results demonstrate that our proposed method consistently outperforms the state-of-the-art baselines on Top-N recommendations.
- Jiajun Bu, Shulong Tan, Chun Chen, Can Wang, Hao Wu, Lijun Zhang, and Xiaofei He. 2010. Music Recommendation by Unified Hypergraph: Combining Social Media Information and Music Content. In Proceedings of the 18th ACM International Conference on Multimedia (Firenze, Italy) (MM ’10). Association for Computing Machinery, New York, NY, USA, 391–400.Google ScholarDigital Library
- Desheng Cai, Shengsheng Qian, Quan Fang, Jun Hu, Wenkui Ding, and Changsheng Xu. 2022. Heterogeneous Graph Contrastive Learning Network for Personalized Micro-video Recommendation. IEEE Transactions on Multimedia(2022), 1–1. https://doi.org/10.1109/TMM.2022.3151026Google ScholarDigital Library
- Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, and Bin Wang. 2021. Bipartite Graph Embedding via Mutual Information Maximization. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21). Association for Computing Machinery, New York, NY, USA, 635–643.Google ScholarDigital Library
- Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event(Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597–1607.Google Scholar
- Xu Chen, Kun Xiong, Yongfeng Zhang, Long Xia, Dawei Yin, and Jimmy Xiangji Huang. 2020. Neural Feature-Aware Recommendation with Signed Hypergraph Convolutional Network. ACM Trans. Inf. Syst. 39, 1, Article 8, 22 pages.Google Scholar
- Yu-An Chung, Yu Zhang, Wei Han, Chung-Cheng Chiu, James Qin, Ruoming Pang, and Yonghui Wu. 2021. w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training. In 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). 244–250.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin. 2022. A Graph Neural Network Framework for Social Recommendations. IEEE Transactions on Knowledge and Data Engineering 34, 5(2022), 2033–2047.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Li Gao, Jia Wu, Zhi Qiao, Chuan Zhou, Hong Yang, and Yue Hu. 2016. Collaborative Social Group Influence for Event Recommendation. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management(Indianapolis, Indiana, USA) (CIKM ’16). Association for Computing Machinery, New York, NY, USA, 1941–1944.Google ScholarDigital Library
- 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.Google Scholar
- Zhiwei Guo and Heng Wang. 2021. A Deep Graph Neural Network-Based Mechanism for Social Recommendations. IEEE Transactions on Industrial Informatics 17, 4 (2021), 2776–2783.Google ScholarCross Ref
- William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs(NIPS’17). 1025–1035.Google Scholar
- Jiadi Han, Qian Tao, Yufei Tang, and Yuhan Xia. 2022. DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 2190–2194.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017, Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 173–182.Google ScholarDigital Library
- Chih-Hui Ho and Nuno Vasconcelos. 2020. Contrastive Learning with Adversarial Examples. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.).Google Scholar
- Qianjiang Hu, Xiao Wang, Wei Hu, and Guo-Jun Qi. 2021. AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1074–1083.Google ScholarCross Ref
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In 2008 Eighth IEEE International Conference on Data Mining. 263–272.Google ScholarDigital Library
- Huan Zhao and Xiaogang Xu and Yangqiu Song and Dik Lun Lee and Zhao Chen and Han Gao. 2021. Ranking Users in Social Networks with Motif-Based PageRank. IEEE Trans. Knowl. Data Eng. 33, 5 (2021), 2179–2192.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Médini (Eds.). ACM, 2320–2329.Google ScholarDigital Library
- Chun-Yi Liu, Chuan Zhou, Jia Wu, Yue Hu, and Li Guo. 2018. Social Recommendation with an Essential Preference Space. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 346–353.Google ScholarCross Ref
- Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jian Yu, and Michael K. Ng. 2021. Bayesian Additive Matrix Approximation for Social Recommendation. ACM Trans. Knowl. Discov. Data 16, 1, Article 7(jul 2021), 34 pages.Google Scholar
- Xiaoling Long, Chao Huang, Yong Xu, Huance Xu, Peng Dai, Lianghao Xia, and Liefeng Bo. 2021. Social Recommendation with Self-Supervised Metagraph Informax Network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management(Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 1160–1169. https://doi.org/10.1145/3459637.3482480Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. Network Motifs: Simple Building Blocks of Complex Networks. Science 298(2002).Google Scholar
- Yi Ouyang, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, and Zhiwen Yu. 2021. Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network. ACM Trans. Knowl. Discov. Data 15, 4, Article 55(apr 2021), 21 pages.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009, Jeff A. Bilmes and Andrew Y. Ng (Eds.). AUAI Press, 452–461.Google ScholarDigital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007. Curran Associates, Inc., 1257–1264.Google Scholar
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. 2002. Incremental singular value decomposition algorithms for highly scalable recommender systems. (2002).Google Scholar
- Changhao Song, Bo Wang, Qinxue Jiang, Yehua Zhang, Ruifang He, and Yuexian Hou. 2021. Social Recommendation with Implicit Social Influence(SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 1788–1792.Google Scholar
- Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Philip S. Yu, and Lifang He. 2021. SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism. In Proceedings of the Web Conference 2021(Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY, USA, 2081–2091.Google ScholarDigital Library
- Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation Learning with Contrastive Predictive Coding. CoRR abs/1807.03748(2018). arXiv:1807.03748Google Scholar
- Hadrien Van Lierde and Tommy W. S. Chow. 2017. A Hypergraph Model for Incorporating Social Interactions in Collaborative Filtering. In Proceedings of the 2017 International Conference on Data Mining, Communications and Information Technology (Phuket, Thailand) (DMCIT ’17). Association for Computing Machinery, New York, NY, USA, Article 32, 6 pages.Google Scholar
- Tongzhou Wang and Phillip Isola. 2020. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event(Proceedings of Machine Learning Research, Vol. 119). PMLR, 9929–9939.Google Scholar
- 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.Google Scholar
- Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. Association for Computing Machinery, New York, NY, USA, 1001–1010.Google Scholar
- Hanrui Wu and Michael K. Ng. 2022. Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification. ACM Trans. Knowl. Discov. Data 16, 4, Article 80(jan 2022), 19 pages.Google ScholarDigital Library
- Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-Supervised Graph Learning for Recommendation. Association for Computing Machinery, New York, NY, USA, 726–735.Google Scholar
- 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 (2020), 1–1.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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]Google Scholar
- 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.Google Scholar
- Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Huang. 2022. Hypergraph Contrastive Collaborative Filtering. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 70–79. https://doi.org/10.1145/3477495.3532058Google ScholarDigital Library
- Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. 2021. Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 4503–4511.Google ScholarCross Ref
- Liangwei Yang, Zhiwei Liu, Yingtong Dou, Jing Ma, and Philip S. Yu. 2021. ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 2141–2145.Google ScholarDigital Library
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 974–983.Google ScholarDigital Library
- Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-Aware Self-Supervised Tri-Training for Recommendation. Association for Computing Machinery, New York, NY, USA, 2084–2092.Google ScholarDigital Library
- Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, and Lizhen Cui. 2020. Enhance Social Recommendation with Adversarial Graph Convolutional Networks. IEEE Transactions on Knowledge and Data Engineering (2020), 1–1.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are Graph Augmentations Necessary? Simple Graph Contrastivenbsp;Learning for Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 1294–1303.Google ScholarDigital Library
- Wei Yu and Shijun Li. 2018. Recommender systems based on multiple social networks correlation. Future Gener. Comput. Syst. 87, 312–327.Google ScholarDigital Library
- Chengyuan Zhang, Yang Wang, Lei Zhu, Jiayu Song, and Hongzhi Yin. 2021. Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation. ACM Trans. Inf. Syst. 40, 2, Article 28 (sep 2021), 26 pages. https://doi.org/10.1145/3466641Google ScholarDigital Library
- Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, and Hongzhi Yin. 2021. Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management(Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 2557–2567. https://doi.org/10.1145/3459637.3482426Google ScholarDigital Library
- Huan Zhao, Xiaogang Xu, Yangqiu Song, Dik Lun Lee, Zhao Chen, and Han Gao. 2018. Ranking Users in Social Networks With Higher-Order Structures. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018. AAAI Press, 232–240.Google ScholarCross Ref
- Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf. 2006. Learning with Hypergraphs: Clustering, Classification, and Embedding. In Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006, Bernhard Schölkopf, John C. Platt, and Thomas Hofmann (Eds.). MIT Press, 1601–1608.Google Scholar
- Tianyu Zhu, Guannan Liu, and Guoqing Chen. 2020. Social Collaborative Mutual Learning for Item Recommendation. ACM Trans. Knowl. Discov. Data 14, 4, Article 43(may 2020), 19 pages.Google ScholarDigital Library
- Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, and Yong Li. 2021. Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks. CoRR abs/2111.03344.Google Scholar
Index Terms
- Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations
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