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Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering

Published: 07 July 2022 Publication History

Abstract

Recently, graph neural networks (GNN) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. However, existing GNN-based CF models suffer from noisy user-item interaction data, which seriously affects the effectiveness and robustness in real-world applications. Although there have been several studies on data denoising in recommender systems, they either neglect direct intervention of noisy interaction in the message-propagation of GNN, or fail to preserve the diversity of recommendation when denoising.
To tackle the above issues, this paper presents a novel GNN-based CF model, named Robust Graph Collaborative Filtering (RGCF), to denoise unreliable interactions for recommendation. Specifically, RGCF consists of a graph denoising module and a diversity preserving module. The graph denoising module is designed for reducing the impact of noisy interactions on the representation learning of GNN, by adopting both a hard denoising strategy (i.e., discarding interactions that are confidently estimated as noise) and a soft denoising strategy (i.e., assigning reliability weights for each remaining interaction). In the diversity preserving module, we build up a diversity augmented graph and propose an auxiliary self-supervised task based on mutual information maximization (MIM) for enhancing the denoised representation and preserving the diversity of recommendation. These two modules are integrated in a multi-task learning manner that jointly improves the recommendation performance. We conduct extensive experiments on three real-world datasets and three synthesized datasets. Experiment results show that RGCF is more robust against noisy interactions and achieves significant improvement compared with baseline models.

Supplementary Material

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The presentation video for "SIGIR 2022 - Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering".

References

[1]
Fabio Aiolli. 2013. Efficient top-n recommendation for very large scale binary rated datasets. In Proceedings of the 7th ACM conference on Recommender systems. 273--280.
[2]
Asim Ansari, Skander Essegaier, and Rajeev Kohli. 2000. Internet recommendation systems. Journal of Marketing Research, Vol. 37, 3 (2000), 363--375.
[3]
Shumeet Baluja, Rohan Seth, Dharshi Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, and Mohamed Aly. 2008. Video suggestion and discovery for youtube: taking random walks through the view graph. In Proceedings of the 17th international conference on World Wide Web. 895--904.
[4]
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).
[5]
Huiyuan Chen, Lan Wang, Yusan Lin, Chin-Chia Michael Yeh, Fei Wang, and Hao Yang. 2021. Structured graph convolutional networks with stochastic masks for recommender systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 614--623.
[6]
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.
[7]
Ping Feng, Yang Qian, Xiaohan Liu, Guoliang Li, and Jian Zhao. 2021. Robust Graph Collaborative Filtering Algorithm Based on Hierarchical Attention. In International Conference on Web Information Systems and Applications . 625--632.
[8]
Marco Gori, Augusto Pucci, V Roma, and I Siena. 2007. Itemrank: A random-walk based scoring algorithm for recommender engines. In IJCAI, Vol. 7. 2766--2771.
[9]
Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014. Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, Vol. 42, 4 (2014), 767--799.
[10]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017).
[11]
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 . 639--648.
[12]
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 355--364.
[13]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining. Ieee, 263--272.
[14]
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 66--74.
[15]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR '17).
[16]
Balaji Lakshminarayanan, Guillaume Bouchard, and Cedric Archambeau. 2011. Robust Bayesian matrix factorisation. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 425--433.
[17]
Shyong K Lam and John Riedl. 2004. Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web. 393--402.
[18]
John Boaz Lee, Ryan A Rossi, Sungchul Kim, Nesreen K Ahmed, and Eunyee Koh. 2019. Attention models in graphs: A survey. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 13, 6 (2019), 1--25.
[19]
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik. 2016. Data poisoning attacks on factorization-based collaborative filtering. Advances in neural information processing systems, Vol. 29 (2016).
[20]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.
[21]
Yile Liang, Tieyun Qian, Qing Li, and Hongzhi Yin. 2021. Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 747--756.
[22]
Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In WWW.
[23]
Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang Song, and Chenliang Li. 2021. Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1099--1108.
[24]
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang. 2021. Learning to drop: Robust graph neural network via topological denoising. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 779--787.
[25]
Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. 2021. UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1253--1262.
[26]
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology, Vol. 27, 1 (2001), 415--444.
[27]
Bhaskar Mehta, Thomas Hofmann, and Wolfgang Nejdl. 2007. Robust collaborative filtering. In Proceedings of the 2007 ACM conference on Recommender systems. 49--56.
[28]
Bhaskar Mehta and Wolfgang Nejdl. 2008. Attack resistant collaborative filtering. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval . 75--82.
[29]
Michael P O'Mahony, Neil J Hurley, and Guénolé CM Silvestre. 2004. Efficient and secure collaborative filtering through intelligent neighbour selection. In ECAI, Vol. 16. 383.
[30]
Michael P O'Mahony, Neil J Hurley, and Guénolé CM Silvestre. 2006. Detecting noise in recommender system databases. In Proceedings of the 11th international conference on Intelligent user interfaces. 109--115.
[31]
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).
[32]
Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. 2020. Graph representation learning via graphical mutual information maximization. In Proceedings of The Web Conference 2020. 259--270.
[33]
Shameem A Puthiya Parambath, Nicolas Usunier, and Yves Grandvalet. 2016. A coverage-based approach to recommendation diversity on similarity graph. In Proceedings of the 10th ACM Conference on Recommender Systems. 15--22.
[34]
Yuqi Qin, Pengfei Wang, and Chenliang Li. 2021. The world is binary: Contrastive learning for denoising next basket recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 859--868.
[35]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. AUAI Press, Arlington, Virginia, USA, 452--461.
[36]
Samuel Rey, Santiago Segarra, Reinhard Heckel, and Antonio G Marques. 2021. Untrained Graph Neural Networks for Denoising. arXiv:2109.11700 (2021).
[37]
J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The adaptive web . Springer, 291--324.
[38]
Florian Strub and Jeremie Mary. 2015. Collaborative filtering with stacked denoising autoencoders and sparse inputs. In NIPS workshop .
[39]
Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, Vol. 2009 (2009).
[40]
Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, et almbox. 2020. A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2030--2039.
[41]
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic. 2019. On Mutual Information Maximization for Representation Learning. In International Conference on Learning Representations .
[42]
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph Convolutional Matrix Completion. arXiv preprint arXiv:1706.02263 (2017).
[43]
Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018a. Graph Attention Networks. In International Conference on Learning Representations .
[44]
Petar Velivc ković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018b. Deep Graph Infomax. In International Conference on Learning Representations .
[45]
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021. Denoising implicit feedback for recommendation. In Proceedings of the 14th ACM international conference on web search and data mining. 373--381.
[46]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165--174.
[47]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled graph collaborative filtering. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval . 1001--1010.
[48]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021 b. Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 726--735.
[49]
Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2021 a. A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation. arXiv preprint arXiv:2104.13030 (2021).
[50]
Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, and Lu Guan. 2019. PD-GAN: adversarial learning for personalized diversity-promoting recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 3870--3876.
[51]
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the ninth ACM international conference on web search and data mining . 153--162.
[52]
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, Vol. 32, 1 (2020), 4--24.
[53]
Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. HOP-rec: high-order proximity for implicit recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems. 140--144.
[54]
Rui Ye, Yuqing Hou, Te Lei, Yunxing Zhang, Qing Zhang, Jiale Guo, Huaiwen Wu, and Hengliang Luo. 2021. Dynamic Graph Construction for Improving Diversity of Recommendation. In Fifteenth ACM Conference on Recommender Systems. 651--655.
[55]
Minji Yoon, Théophile Gervet, Baoxu Shi, Sufeng Niu, Qi He, and Jaewon Yang. 2021. Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 2046--2056.
[56]
Hengtong Zhang, Changxin Tian, Yaliang Li, Lu Su, Nan Yang, Wayne Xin Zhao, and Jing Gao. 2021. Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2154--2164.
[57]
Xiang Zhang and Marinka Zitnik. 2020. Gnnguard: Defending graph neural networks against adversarial attacks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9263--9275.
[58]
Wayne Xin Zhao, Junhua Chen, Pengfei Wang, Qi Gu, and Ji-Rong Wen. 2020. Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2329--2332.
[59]
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et almbox. 2021. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4653--4664.
[60]
Jiawei Zheng, Qianli Ma, Hao Gu, and Zhenjing Zheng. 2021. Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2338--2348.
[61]
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 1893--1902.

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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
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    Published: 07 July 2022

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    Author Tags

    1. denoising
    2. graph neural networks
    3. recommender systems

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    • (2025)Beyond Users: Denoising Behavior-based Contrastive Learning for Disentangled Cross-Domain RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_11(163-178)Online publication date: 11-Jan-2025
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