tutorial

Self-Supervised Learning for Recommendation

Authors:

Dawei YinAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 5136 - 5139

https://doi.org/10.1145/3511808.3557506

Published: 17 October 2022 Publication History

Abstract

Recommender systems are playing an increasingly critical role to alleviate information overload and satisfy users' information seeking requirements in a wide spectrum of online platforms. However, the ubiquity of data sparsity and noise notably limits the representation capacity of existing recommender systems to learn high-quality user (item) embeddings. Inspired by recent advances of self-supervised learning (SSL) techniques, SSL-based representation learning models benefit a variety of recommendation domains. Such methods have achieved new levels of performance while reducing the dependence on observed supervision labels in diverse recommendation tasks. In this tutorial, we aim to provide a systemic review of state-of-the-art SSL-based recommender systems. To be specific, we summarize and categorize existing work of SSL-based recommender systems in terms of recommendation scenarios. For each type of recommendation task, the corresponding challenges and methods will be presented in a comprehensive way. Finally, some future directions and open questions will be raised to inspire more investigation on this important research line.

References

[1]

Rahul Bhagat, Srevatsan Muralidharan, Alex Lobzhanidze, and Shankar Vishwanath. 2018. Buy it again: Modeling repeat purchase recommendations. In International Conference on Knowledge Discovery & Data Mining (KDD). 62--70.

Digital Library

[2]

Chong Chen, Min Zhang, et al. 2019a. Social attentional memory network: Modeling aspect-and friend-level differences in recommendation. In International Conference on Web Search and Data Mining (WSDM). 177--185.

Digital Library

[3]

Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, and Shaoping Ma. 2019b. An efficient adaptive transfer neural network for social-aware recommendation. In International Conference on Research and Development in Information Retrieval (SIGIR). 225--234.

Digital Library

[4]

Lei Chen, Le Wu, Richang Hong, Kun Zhang, et al. 2020. Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. In International Conference on Artificial Intelligence (AAAI), Vol. 34. 27--34.

[5]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, et al. 2019. Graph neural networks for social recommendation. In The World Wide Web Conference (WWW). 417--426.

Digital Library

[6]

Shanshan Feng, Lucas Vinh Tran, Gao Cong, et al. 2020. Hme: A hyperbolic metric embedding approach for next-poi recommendation. In International Conference on Research and Development in Information Retrieval (SIGIR). 1429--1438.

Digital Library

[7]

Shuyun Gu, Xiao Wang, Chuan Shi, and Ding Xiao. 2022. Self-supervised Graph Neural Networks for Multi-behavior Recommendation. In International Joint Conference on Artificial Intelligence (IJCAI).

[8]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In International Joint Conference on Artificial Intelligence (IJCAI). 1725--1731.

[9]

Ruocheng Guo, Xiaoting Zhao, Adam Henderson, Liangjie Hong, and Huan Liu. 2020. Debiasing grid-based product search in e-commerce. In International Conference on Knowledge Discovery & Data Mining (KDD). 2852--2860.

Digital Library

[10]

Li He, Hongxu Chen, Dingxian Wang, Shoaib Jameel, Philip Yu, et al. 2021. Click-Through Rate Prediction with Multi-Modal Hypergraphs. In International Conference on Information & Knowledge Management (CIKM). 690--699.

[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 International Conference on Research and Development in Information Retrieval (SIGIR). 639--648.

Digital Library

[12]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, et al. 2017. Neural collaborative filtering. In International World Wide Web Conference (WWW). 173--182.

Digital Library

[13]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. International Conference on Learning Representations (ICLR).

[14]

Chao Huang. 2021. Recent advances in heterogeneous relation learning for recommendation. arXiv preprint arXiv:2110.03455 (2021).

[15]

Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, and Yanfang Ye. 2021. Knowledge-aware coupled graph neural network for social recommendation. In International Conference on Artificial Intelligence (AAAI), Vol. 35. 4115--4122.

[16]

Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2020. Multi-behavior recommendation with graph convolutional networks. In International Conference on Research and Development in Information Retrieval (SIGIR). 659--668.

Digital Library

[17]

Yehuda Koren, Steffen Rendle, and Robert Bell. 2022. Advances in collaborative filtering. Recommender systems handbook (2022), 91--142.

[18]

Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In ACM Web Conference (WWW). 2320--2329.

[19]

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. 1160--1169.

[20]

Zhiqiang Pan and Honghui Chen. 2021. Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions. Mathematics, Vol. 9, 17 (2021), 2129.

[21]

Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022. Contrastive learning for representation degeneration problem in sequential recommendation. In International Conference on Web Search and Data Mining (WSDM). 813--823.

Digital Library

[22]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In International Conference on Web Search and Data Mining (WSDM). 565--573.

Digital Library

[23]

Heyuan Wang, Fangzhao Wu, Zheng Liu, and Xing Xie. 2020. Fine-grained interest matching for neural news recommendation. In Annual Meeting of the Association for Computational Linguistics (ACL). 836--845.

[24]

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019c. Knowledge graph convolutional networks for recommender systems. In The World Wide Web Conference (WWW). 3307--3313.

Digital Library

[25]

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019a. Kgat: Knowledge graph attention network for recommendation. In International Conference on Knowledge Discovery & Data Mining (KDD). 950--958.

Digital Library

[26]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019b. Neural graph collaborative filtering. In International Conference on Research and Development in Information Retrieval (SIGIR). 165--174.

Digital Library

[27]

Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. 2022. Contrastive meta learning with behavior multiplicity for recommendation. In International Conference on Web Search and Data Mining (WSDM). 1120--1128.

Digital Library

[28]

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, et al. 2021. Self-supervised graph learning for recommendation. In International Conference on Research and Development in Information Retrieval (SIGIR). 726--735.

Digital Library

[29]

Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019b. A neural influence diffusion model for social recommendation. In International Conference on Research and Development in Information Retrieval (SIGIR). 235--244.

Digital Library

[30]

Yuxia Wu, Ke Li, Guoshuai Zhao, and Xueming Qian. 2019a. Long-and short-term preference learning for next POI recommendation. In International Conference on Information & Knowledge Management (CIKM). 2301--2304.

Digital Library

[31]

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, and Liefeng Bo. 2020. Multiplex behavioral relation learning for recommendation via memory augmented transformer network. In International Conference on Research and Development in Information Retrieval (SIGIR). 2397--2406.

Digital Library

[32]

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, and Liefeng Bo. 2021a. Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In International Conference on Artificial Intelligence (AAAI), Vol. 35. 4486--4493.

[33]

Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Huang. 2022b. Hypergraph contrastive collaborative filtering. In International Conference on Research and Development in Information Retrieval (SIGIR). 70--79.

Digital Library

[34]

Lianghao Xia, Chao Huang, and Chuxu Zhang. 2022a. Self-Supervised Hypergraph Transformer for Recommender Systems. In International Conference on Knowledge Discovery and Data Mining (KDD). 2100--2109.

[35]

Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, and Liefeng Bo. 2021b. Graph meta network for multi-behavior recommendation. In International Conference on Research and Development in Information Retrieval (SIGIR). 757--766.

Digital Library

[36]

Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In International Conference on Data Engineering (ICDE). IEEE, 1259--1273.

[37]

Haoran Yang, Hongxu Chen, Lin Li, S Yu Philip, and Guandong Xu. 2021. Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation. In International Conference on Data Mining (ICDM). IEEE, 787--796.

[38]

Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge Graph Contrastive Learning for Recommendation. arXiv preprint arXiv:2205.00976 (2022).

[39]

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, et al. 2018. Graph convolutional neural networks for web-scale recommender systems. In International Conference on Knowledge Discovery & Data Mining (KDD). 974--983.

Digital Library

[40]

Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, et al. 2021a. Socially-aware self-supervised tri-training for recommendation. In International Conference on Knowledge Discovery & Data Mining (KDD). 2084--2092.

Digital Library

[41]

Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, et al. 2021b. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In The Web Conference (WWW). 413--424.

[42]

Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang, Zhongying Zhao, and Junyu Dong. 2022. Multiplex Heterogeneous Graph Convolutional Network. In International Conference on Knowledge Discovery and Data Mining (KDD). 2377--2387.

[43]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, et al. 2016. Collaborative knowledge base embedding for recommender systems. In International Conference on Knowledge Discovery and Data Mining (KDD). 353--362.

Digital Library

[44]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), Vol. 52, 1 (2019), 1--38.

Digital Library

[45]

Weifeng Zhang, Jingwen Mao, Yi Cao, and Congfu Xu. 2020. Multiplex graph neural networks for multi-behavior recommendation. In International Conference on Information & Knowledge Management (CIKM). 2313--2316.

Digital Library

[46]

Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2018. Deep reinforcement learning for page-wise recommendations. In ACM Conference on Recommender Systems (Recsys). 95--103.

Digital Library

[47]

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, et al. 2019. Deep interest evolution network for click-through rate prediction. In International Conference on Artificial Intelligence (AAAI), Vol. 33. 5941--5948.

Digital Library

[48]

Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, et al. 2020. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM. 1893--1902.

[49]

Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. 2022. Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System. In International Conference on Research and Development in Information Retrieval (SIGIR).

Cited By

Dong JZhou YHao SFeng DZheng HXu Z(2024)Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive LearningMathematics10.3390/math1213205712:13(2057)Online publication date: 30-Jun-2024
https://doi.org/10.3390/math12132057
Malitesta DCornacchia GPomo CMerra FDi Noia TDi Sciascio E(2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3662738
Soudani HPetcu RKanoulas EHasibi FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Data Augmentation for Conversational AICompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641238(1234-1237)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641238
Show More Cited By

Index Terms

Self-Supervised Learning for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Dong JZhou YHao SFeng DZheng HXu Z(2024)Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive LearningMathematics10.3390/math1213205712:13(2057)Online publication date: 30-Jun-2024
https://doi.org/10.3390/math12132057
Malitesta DCornacchia GPomo CMerra FDi Noia TDi Sciascio E(2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3662738
Soudani HPetcu RKanoulas EHasibi FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Data Augmentation for Conversational AICompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641238(1234-1237)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641238
Gan MZhang XLiang Y(2024)CGG: Category-aware global graph contrastive learning for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112661305(112661)Online publication date: Dec-2024
https://doi.org/10.1016/j.knosys.2024.112661
Guo ZSong PFeng CYao KDang CLiang J(2024)Causal intervention for knowledge graph denoising in recommender systemsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02500-0Online publication date: 21-Dec-2024
https://doi.org/10.1007/s13042-024-02500-0
Anelli VMalitesta DPomo CBellogin ADi Sciascio EDi Noia T(2023)Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven AnalysisProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609489(350-361)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3609489
Liu XMeng SLi QQi LXu XDou WZhang XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615063(1566-1575)Online publication date: 21-Oct-2023
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Ariannezhad MLi MJullien Sde Rijke MChen HDuh WHuang HKato MMothe JPoblete B(2023)Complex Item Set RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594248(3444-3447)Online publication date: 19-Jul-2023
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Liu XMeng SLi QXu XQi LDou WZhang JZhang X(2023)Disentangled Hypergraph Collaborative Filtering for Social Recommendation2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00066(475-482)Online publication date: Jul-2023
https://doi.org/10.1109/ICWS60048.2023.00066

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