skip to main content
10.1145/3539597.3570483acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article

Knowledge-Adaptive Contrastive Learning for Recommendation

Published: 27 February 2023 Publication History

Abstract

By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.

References

[1]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013).
[2]
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In WWW. 151--161.
[3]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191--198.
[4]
Michael Gutmann and Aapo Hyvärinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 297--304.
[5]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639--648.
[6]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.
[7]
Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S Yu. 2018. Leveraging metapath based context for top-n recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1531--1540.
[8]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014).
[9]
Thomas N Kipf and MaxWelling. 2016. Semi-supervised classification with graph convolutional networks. ICLR (2016).
[10]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37.
[11]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI.
[12]
Dongsheng Luo,Wei Cheng, Dongkuan Xu,Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized Explainer for Graph Neural Network. Advances in Neural Information Processing Systems 33 (2020).
[13]
Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. 2021. Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks. (2021).
[14]
Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, and Mark Coates. 2021. Knowledge-enhanced top-k recommendation in poincaré ball. arXiv preprint arXiv:2101.04852 (2021).
[15]
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. arXiv:1611.00712 [cs.LG]
[16]
Zhiqiang Pan and Honghui Chen. 2021. Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions. Mathematics 9, 17 (2021), 2129.
[17]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[18]
Riku Togashi, Mayu Otani, and Shin'ichi Satoh. 2021. Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 931--939.
[19]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. ICLR 2 (2018).
[20]
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 417--426.
[21]
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In WWW. 1835--1844.
[22]
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and ZhongyuanWang. 2019. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 968--977.
[23]
HongweiWang, Miao Zhao, Xing Xie,Wenjie Li, and Minyi Guo. 2019. Knowledge Graph Convolutional Networks for Recommender Systems. InWWW. 3307--3313.
[24]
JizheWang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018. Billion-scale commodity embedding for e-commerce recommendation in alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 839--848.
[25]
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 950--958.
[26]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.
[27]
Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua. 2021. Learning Intents behind Interactions with Knowledge Graph for Recommendation. In Proceedings of the Web Conference 2021. 878--887.
[28]
XiangWang, DingxianWang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019. Explainable reasoning over knowledge graphs for recommendation. In AAAI, Vol. 33. 5329--5336.
[29]
Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, and Tat-Seng Chua. 2020. Reinforced negative sampling over knowledge graph for recommendation. In Proceedings of The Web Conference 2020. 99--109.
[30]
Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, and Philip S Yu. 2021. Dskreg: Differentiable sampling on knowledge graph for recommendation with relational gnn. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3513--3517.
[31]
Ze Wang, Guangyan Lin, Huobin Tan, Qinghong Chen, and Xiyang Liu. 2020. CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems. In SIGIR. 219--228.
[32]
Markus Weimer, Alexandros Karatzoglou, and Alex Smola. 2008. Adaptive collaborative filtering. In Proceedings of the 2008 ACM conference on Recommender systems. 275--282.
[33]
JiancanWu, XiangWang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.
[34]
YiqingWu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, and Qing He. 2022. Multi-view Multi-behavior Contrastive Learning in Recommendation. arXiv preprint arXiv:2203.10576 (2022).
[35]
Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, and Liefeng Bo. 2021. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In AAAI, Vol. 35. 4486-- 4493.
[36]
Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, and Lizhen Cui. 2021. Self- Supervised Graph Co-Training for Session-based Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2180--2190.
[37]
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In International Conference on Machine Learning. PMLR, 5453--5462.
[38]
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014).
[39]
Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge Graph Contrastive Learning for Recommendation. arXiv preprint arXiv:2205.00976 (2022).
[40]
Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-Aware Self-Supervised Tri-Training for Recommendation. arXiv preprint arXiv:2106.03569 (2021).
[41]
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. 413--424.
[42]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation. In SIGIR. 1294--1303.
[43]
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353--362.
[44]
Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A deep reinforcement learning framework for news recommendation. In WWW. 167--176.
[45]
Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. 2022. Multi-level Cross-viewContrastive Learning for Knowledge-aware Recommender System. arXiv preprint arXiv:2204.08807 (2022).
[46]
Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, and Dangyang Chen. 2022. Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning. In CIKM.

Cited By

View all
  • (2025)Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for RecommendationTsinghua Science and Technology10.26599/TST.2023.901014330:2(585-599)Online publication date: Apr-2025
  • (2025)KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?ACM Transactions on Information Systems10.1145/3713071Online publication date: 21-Jan-2025
  • (2025)PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemACM Transactions on Information Systems10.1145/370834443:2(1-24)Online publication date: 28-Jan-2025
  • Show More Cited By

Index Terms

  1. Knowledge-Adaptive Contrastive Learning for Recommendation

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
    February 2023
    1345 pages
    ISBN:9781450394079
    DOI:10.1145/3539597
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 February 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. contrastive learning
    2. graph neural networks
    3. knowledge graph
    4. recommender system

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    WSDM '23

    Acceptance Rates

    Overall Acceptance Rate 498 of 2,863 submissions, 17%

    Upcoming Conference

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)503
    • Downloads (Last 6 weeks)18
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for RecommendationTsinghua Science and Technology10.26599/TST.2023.901014330:2(585-599)Online publication date: Apr-2025
    • (2025)KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?ACM Transactions on Information Systems10.1145/3713071Online publication date: 21-Jan-2025
    • (2025)PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemACM Transactions on Information Systems10.1145/370834443:2(1-24)Online publication date: 28-Jan-2025
    • (2025)DMR: disentangled and denoised learning for multi-behavior recommendationComplex & Intelligent Systems10.1007/s40747-024-01778-511:2Online publication date: 16-Jan-2025
    • (2025)Self-augmented Contrastive Learning for Knowledge-Aware RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_17(261-276)Online publication date: 12-Jan-2025
    • (2024)A feature-enhanced knowledge graph neural network for machine learning method recommendationPeerJ Computer Science10.7717/peerj-cs.228410(e2284)Online publication date: 28-Aug-2024
    • (2024)ReCRec: Reasoning the Causes of Implicit Feedback for Debiased RecommendationACM Transactions on Information Systems10.1145/367227542:6(1-26)Online publication date: 18-Oct-2024
    • (2024)Improving Faithfulness and Factuality with Contrastive Learning in Explainable RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365398416:1(1-23)Online publication date: 26-Dec-2024
    • (2024)Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph ReasoningACM Transactions on Knowledge Discovery from Data10.1145/364836618:6(1-19)Online publication date: 12-Apr-2024
    • (2024)Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688186(1016-1021)Online publication date: 8-Oct-2024
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media