research-article

Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation

Authors:

Satoshi Nakagawa,

Fuji RenAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 793 - 802

https://doi.org/10.1145/3583780.3614806

Published: 21 October 2023 Publication History

Abstract

Social networks exhibit a distinct "celebrity effect" whereby influential individuals have a more significant impact on others compared to ordinary individuals, unlike other network structures such as citation networks and knowledge graphs. Despite its common occurrence in social networks, the celebrity effect is frequently overlooked by existing social recommendation methods when modeling social relationships, thereby hindering the full exploitation of social networks to mine similarities between users. In this paper, we fill this gap and propose a Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation (CGCL), which explicitly models the celebrity effect in the social domain. Technically, we measure the different influences of celebrity and ordinary nodes by mining social network structure features, such as closeness centrality. To model the celebrity effect in social networks, we design a novel user-user impact-aware aggregation method, which incorporates the celebrity-aware influence information into the message propagation process. Additionally, we design a graph neural network-based framework which incorporates social semantics into the user-item interaction modeling with contrastive learning-enhanced data augmentation. The experimental results on three real-world datasets show the effectiveness of the proposed framework. We conduct ablation experiments to prove that the key components of our model benefit the recommendation performance improvement.

References

[1]

Lorenzo Bizzi and Alice Labban. 2019. The double-edged impact of social media on online trading: Opportunities, threats, and recommendations for organizations. Business Horizons, Vol. 62, 4 (2019), 509--519.

[2]

Hamed R. Bonab, Mohammad Aliannejadi, Ali Vardasbi, Evangelos Kanoulas, and James Allan. 2021. Cross-Market Product Recommendation. In CIKM. ACM, 110--119.

[3]

Phillip Bonacich. 1987. Power and centrality: A family of measures. Amer. J. Sociology, Vol. 92, 5 (1987), 1170--1182.

[4]

Sergey Brin and Lawrence Page. 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Comput. Networks, Vol. 30, 1--7 (1998), 107--117.

Digital Library

[5]

ALAN J. BUSH, CRAIG A. MARTIN, and VICTORIA D. BUSH. 2004. Sports Celebrity Influence on the Behavioral Intentions of Generation Y. Journal of Advertising Research, Vol. 44, 1 (2004), 108--118. https://doi.org/10.1017/S0021849904040206

[6]

Jiangxia Cao, Xin Cong, Tingwen Liu, and Bin Wang. 2022. Item Similarity Mining for Multi-Market Recommendation. In SIGIR. ACM, 2249--2254.

[7]

Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021. Sequential Recommendation with Graph Neural Networks. In SIGIR. ACM, 378--387.

[8]

Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, and Ronghua Luo. 2023. Heterogeneous Graph Contrastive Learning for Recommendation. In WSDM. ACM, 544--552.

[9]

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In ICML (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607.

[10]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In WWW. ACM, 417--426.

[11]

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 Trans. Knowl. Data Eng., Vol. 34, 5 (2022), 2033--2047.

[12]

Benson P. Fraser and William J. Brown. 2002. Media, Celebrities, and Social Influence: Identification With Elvis Presley. Mass Communication and Society, Vol. 5, 2 (2002), 183--206.

[13]

Linton C Freeman. 1977. A set of measures of centrality based on betweenness. Sociometry (1977), 35--41.

[14]

Linton C Freeman et al. 2002. Centrality in social networks: Conceptual clarification. Social Network: Critical Concepts in Sociology. Londres: Routledge, Vol. 1 (2002), 238--263.

[15]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS (JMLR Proceedings, Vol. 9). JMLR.org, 249--256.

[16]

Michael Gutmann and Aapo Hyv"a rinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS (JMLR Proceedings, Vol. 9). JMLR.org, 297--304.

[17]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. ACM, 639--648.

[18]

Guang-Neng Hu, Xin-Yu Dai, Yunya Song, Shujian Huang, and Jiajun Chen. 2015. A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews. In IJCAI. AAAI Press, 1756--1762.

[19]

Yanbin Jiang, Huifang Ma, Yuhang Liu, Zhixin Li, and Liang Chang. 2021. Enhancing social recommendation via two-level graph attentional networks. Neurocomputing, Vol. 449 (2021), 71--84.

[20]

Irwin King, Michael R. Lyu, and Hao Ma. 2010. Introduction to social recommendation. In WWW. ACM, 1355--1356.

[21]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster). OpenReview.net.

[22]

Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, and Tat-Seng Chua. 2022. Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. In ICML (Proceedings of Machine Learning Research, Vol. 162). PMLR, 13052--13065.

[23]

Jie Liao, Wei Zhou, Fengji Luo, Junhao Wen, Min Gao, Xiuhua Li, and Jun Zeng. 2022. SocialLGN: Light graph convolution network for social recommendation. Inf. Sci., Vol. 589 (2022), 595--607.

Digital Library

[24]

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 CIKM. ACM, 1160--1169.

[25]

Kazuyuki Matsumoto, Fuji Ren, Masaya Matsuoka, Minoru Yoshida, and Kenji Kita. 2019. Slang feature extraction by analysing topic change on social media. CAAI Trans. Intell. Technol., Vol. 4, 1 (2019), 64--71.

Digital Library

[26]

Marcela Moraes, John Gountas, Sandra Gountas, and Piyush Sharma. 2019. Celebrity influences on consumer decision making: new insights and research directions. Journal of Marketing Management, Vol. 35, 13--14 (2019), 1159--1192.

[27]

Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab.

[28]

Sejung Park and Cynthia A. Hoffner. 2020. Tweeting about mental health to honor Carrie Fisher: How #InHonorOfCarrie reinforced the social influence of celebrity advocacy. Comput. Hum. Behav., Vol. 110 (2020), 106353.

[29]

Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020. GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. In KDD. ACM, 1150--1160.

Digital Library

[30]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. AUAI Press, 452--461.

Digital Library

[31]

Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. 2002. Methods and metrics for cold-start recommendations. In SIGIR. ACM, 253--260.

[32]

Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, and Srijan Kumar. 2022. A Survey of Graph Neural Networks for Social Recommender Systems. CoRR, Vol. abs/2212.04481 (2022).

[33]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell., Vol. 2009 (2009), 421425:1--421425:19.

[34]

Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial Graph Augmentation to Improve Graph Contrastive Learning. In NeurIPS. 15920--15933.

[35]

Jiliang Tang, Xia Hu, and Huan Liu. 2013. Social recommendation: a review. Soc. Netw. Anal. Min., Vol. 3, 4 (2013), 1113--1133.

[36]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR (Poster). OpenReview.net.

[37]

Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep Graph Infomax. In ICLR (Poster). OpenReview.net.

[38]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In SIGIR. ACM, 165--174.

[39]

Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, and Ke Tang. 2022a. Disentangled Contrastive Learning for Social Recommendation. In CIKM. ACM, 4570--4574.

[40]

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. ACM, 726--735.

[41]

Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, and Meng Wang. 2022b. DiffNet: A Neural Influence and Interest Diffusion Network for Social Recommendation. IEEE Trans. Knowl. Data Eng., Vol. 34, 10 (2022), 4753--4766.

Digital Library

[42]

Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019a. A Neural Influence Diffusion Model for Social Recommendation. In SIGIR. ACM, 235--244.

[43]

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019b. Session-Based Recommendation with Graph Neural Networks. In AAAI. AAAI Press, 346--353.

Digital Library

[44]

Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph Contrastive Learning with Augmentations. In NeurIPS.

[45]

Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-Aware Self-Supervised Tri-Training for Recommendation. In KDD. ACM, 2084--2092.

Digital Library

[46]

Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, and Lizhen Cui. 2020. Enhancing Social Recommendation with Adversarial Graph Convolutional Networks. IEEE Trans. Knowl. Data Eng., Vol. 34, 8 (2020), 3727--3739.

[47]

Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph Contrastive Learning with Adaptive Augmentation. In WWW. ACM / IW3C2, 2069--2080.

Cited By

Hu ZNakagawa SZhuang YDeng JCai SZhou TRen F(2025)Hierarchical Denoising for Robust Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350877837:2(739-753)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3508778
Anand VMaurya A(2024)A Survey on Recommender Systems Using Graph Neural NetworkACM Transactions on Information Systems10.1145/369478443:1(1-49)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3694784
Li ZWang JChen ZWu KWei YHuang H(2024)Adaptive Graph Neural Networks for Cold-Start Multimedia Recommendation2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00027(201-210)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00027

Index Terms

Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
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Frank Hopfgartner
University of Koblenz, Germany
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Mark Lee
University of Birmingham, UK
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University of Birmingham, UK
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Program Chairs:
Mounia Lalmas
Spotify, UK
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Min Zhang
Tsinghua University, China
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Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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October 21 - 25, 2023

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Cited By

Hu ZNakagawa SZhuang YDeng JCai SZhou TRen F(2025)Hierarchical Denoising for Robust Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350877837:2(739-753)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3508778
Anand VMaurya A(2024)A Survey on Recommender Systems Using Graph Neural NetworkACM Transactions on Information Systems10.1145/369478443:1(1-49)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3694784
Li ZWang JChen ZWu KWei YHuang H(2024)Adaptive Graph Neural Networks for Cold-Start Multimedia Recommendation2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00027(201-210)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00027

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