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Self-derived Knowledge Graph Contrastive Learning for Recommendation

Published: 28 October 2024 Publication History

Abstract

Knowledge Graphs (KGs) serve as valuable auxiliary information to improve the accuracy of recommendation systems. Previous methods have leveraged the knowledge graph to enhance item representation and thus achieve excellent performance. However, these approaches heavily rely on high-quality knowledge graphs and learn enhanced representations with the assistance of carefully designed triplets. Furthermore, the emergence of knowledge graphs has led to models that ignore the inherent relationships between items and entities. To address these challenges, we propose a Self-Derived Knowledge Graph Contrastive Learning framework (CL-SDKG) to enhance recommendation systems. Specifically, we employ the variational graph reconstruction technique to estimate the Gaussian distribution of user-item nodes corresponding to the graph neural network aggregation layer. This process generates multiple KGs, referred to as self-derived KGs. The self-derived KG acquires more robust perceptual representations through the consistency of the estimated structure. Besides, the self-derived KG allows models to focus on user-item interactions and reduce the negative impact of miscellaneous dependencies introduced by conventional KGs. Finally, we apply contrastive learning to the self-derived KG to further improve the robustness of CL-SDKG through the traditional KG contrast-enhanced process. We conducted comprehensive experiments on three public datasets, and the results demonstrate that our CL-SDKG outperforms state-of-the-art baselines.

Supplemental Material

MP4 File - Self-Derived Knowledge Graph Contrastive Learning for Enhanced Recommendation Systems
This paper introduces the Self-Derived Knowledge Graph Contrastive Learning (CL-SDKG) framework, a novel approach for improving recommendation systems. Unlike traditional methods that rely heavily on high-quality knowledge graphs and meticulously designed triplets, CL-SDKG employs variational graph reconstruction to generate self-derived knowledge graphs. These self-derived graphs capture robust user-item interactions while mitigating the impact of extraneous dependencies. By integrating contrastive learning, CL-SDKG further refines these representations, enhancing the system's accuracy and robustness. Experimental results on three public datasets demonstrate that CL-SDKG significantly outperforms existing state-of-the-art methods, providing a more effective and adaptable solution for personalized recommendations.

References

[1]
Farah Atif, Ola El Khatib, and Djellel Difallah. 2023. Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 781--790.
[2]
James Henry Bell, Kallista A Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova. 2020. Secure single-server aggregation with (poly) logarithmic overhead. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 1253--1269.
[3]
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).
[4]
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 The world wide web conference. 151--161.
[5]
Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, and Yang Yang. 2023. DropMessage: Unifying Random Dropping for Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 37, 4 (Jun. 2023), 4267--4275.
[6]
Dieter Fensel, Umutcan 'im'ek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, Alexander Wahler, Dieter Fensel, et al. 2020. Introduction: what is a knowledge graph? Knowledge graphs: Methodology, tools and selected use cases (2020), 1--10.
[7]
Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, and Yong Li. 2023. A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Trans. Recomm. Syst. 1, 1 (2023).
[8]
Amulya Gupta and Zhu Zhang. 2023. Neural Topic Modeling via Discrete Variational Inference. ACM Transactions on Intelligent Systems and Technology 14, 2 (2023), 1--33.
[9]
Wei He, Guohao Sun, Jinhu Lu, and Xiu Susie Fang. 2023. Candidate-aware Graph Contrastive Learning for Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 1670--1679.
[10]
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.
[11]
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. 173--182.
[12]
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.
[13]
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. Proceedings of the AAAI conference on artificial intelligence 35, 5 (2021), 4115--4122.
[14]
Han Huang, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, and Hui Xiong. 2021. Representation learning on knowledge graphs for node importance estimation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 646--655.
[15]
Kezhao Huang, Jidong Zhai, Zhen Zheng, Youngmin Yi, and Xipeng Shen. 2021. Understanding and bridging the gaps in current GNN performance optimizations. In Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 119--132.
[16]
Juanhui Li, Wei Zeng, Suqi Cheng, Yao Ma, Jiliang Tang, Shuaiqiang Wang, and Dawei Yin. 2023. Graph enhanced bert for query understanding. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 3315--3319.
[17]
Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel, and Julian McAuley. 2022. Coarse-to-fine sparse sequential recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2082--2086.
[18]
Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Junlin Liu, and Jun Wang. 2024. Making recommender systems forget: Learning and unlearning for erasable recommendation. Knowledge-Based Systems 283 (2024), 111124.
[19]
Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, and Tao Mei. 2020. Learning the compositional visual coherence for complementary recommendations. arXiv preprint arXiv:2006.04380 (2020).
[20]
Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. 2023. Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). 1559--1568.
[21]
Fan Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, and Mohan Kankanhalli. 2023. Semantic-Guided Feature Distillation for Multimodal Recommendation. In Proceedings of the 31st ACM International Conference on Multimedia (MM '23). Association for Computing Machinery, New York, NY, USA, 6567--6575.
[22]
Xinpeng Liu and Xianqiang Yang. 2023. Exploiting Spike-and-Slab Prior for Variational Estimation of Nonlinear Systems. IEEE Transactions on Industrial Informatics (2023).
[23]
Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. 2021. Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In Proceedings of the web conference 2021. 3168--3177.
[24]
Zemin Liu, Trung-Kien Nguyen, and Yuan Fang. 2021. Tail-gnn: Tail-node graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1109--1119.
[25]
Zi-Feng Mai, Chang-DongWang, Zhongjie Zeng, Ya Li, Jiaquan Chen, and Philip S Yu. 2023. Hypergraph Enhanced Knowledge Tree Prompt Learning for Next- Basket Recommendation. arXiv preprint arXiv:2312.15851 (2023).
[26]
Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, and Eugenio Di Sciascio. 2023. KGTORe: Tailored Recommendations through Knowledge-aware GNN Models. In Proceedings of the 17th ACM Conference on Recommender Systems. 576--587.
[27]
Xichuan Niu, Bofang Li, Chenliang Li, Rong Xiao, Haochuan Sun, Hongbo Deng, and Zhenzhong Chen. 2020. A dual heterogeneous graph attention network to improve long-tail performance for shop search in e-commerce. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3405--3415.
[28]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and LB Schmidt-Thieme. 2014. Bayesian personalized ranking from implicit feedback. In Proc. of Uncertainty in Artificial Intelligence. 452--461.
[29]
Yu Tian, Yuhao Yang, Xudong Ren, PengfeiWang, FangzhaoWu, QianWang, and Chenliang Li. 2021. Joint knowledge pruning and recurrent graph convolution for news recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 51--60.
[30]
Koen Verstrepen, Kanishka Bhaduriy, Boris Cule, and Bart Goethals. 2017. Collaborative Filtering for Binary, Positiveonly Data. SIGKDD Explor. Newsl. 19, 1 (2017), 1--21.
[31]
FanWang, Haibin Zhu, Gautam Srivastava, Shancang Li, Mohammad R Khosravi, and Lianyong Qi. 2021. Robust collaborative filtering recommendation with user-item-trust records. IEEE Transactions on Computational Social Systems 9, 4 (2021), 986--996.
[32]
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.
[33]
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.
[34]
HongweiWang, Miao Zhao, Xing Xie,Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In The world wide web conference. 3307--3313.
[35]
Jianwen Wang and Zhaogong Zhang. 2022. Graph Neural Network with Item Life Cycle for Social Recommendation. Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence, 160--165.
[36]
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.
[37]
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.
[38]
JiancanWu, XiangWang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 726--735.
[39]
Lianghao Xia, Chao Huang, and Chuxu Zhang. 2022. Self-supervised hypergraph transformer for recommender systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2100--2109.
[40]
Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, and Lizhen Cui. 2021. Selfsupervised graph co-training for session-based recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management. 2180--2190.
[41]
Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M Jose, Maarten de Rijke, et al. 2023. Improving implicit feedback-based recommendation through multi-behavior alignment. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval. 932--941.
[42]
Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, and Yongfeng Zhang. 2023. Causal collaborative filtering. Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 235--245.
[43]
Yuhao Yang, Chao Huang, Lianghao Xia, and Chunzhen Huang. 2023. Knowledge graph self-supervised rationalization for recommendation. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. 3046--3056.
[44]
Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1434--1443.
[45]
Yonghui Yang, ZhengweiWu, LeWu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023. Generative-Contrastive Graph Learning for Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 1117--1126.
[46]
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al. 2021. Self-supervised learning for large-scale item recommendations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4321--4330.
[47]
Peiqi Yin, Xiao Yan, Jinjing Zhou, Qiang Fu, Zhenkun Cai, James Cheng, Bo Tang, and Minjie Wang. 2023. Dgi: An easy and efficient framework for gnn model evaluation. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5439--5450.
[48]
Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2023. XSimGCL: Towards extremely simple graph contrastive learning for recommendation. IEEE Transactions on Knowledge and Data Engineering (2023).
[49]
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 Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2084--2092.
[50]
Penghang Yu, Zhiyi Tan, Guanming Lu, and Bing-Kun Bao. 2023. Multi-View Graph Convolutional Network for Multimedia Recommendation. In Proceedings of the 31st ACM International Conference on Multimedia (MM '23). Association for Computing Machinery, New York, NY, USA, 6576--6585.
[51]
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.
[52]
Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, and Siuming Yiu. 2023. Automated Spatio-Temporal Graph Contrastive Learning. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW '23). New York, NY, USA, 295--305.
[53]
Yuxin Zhang, Mingbao Lin, Zhihang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, and Rongrong Ji. 2022. Learning best combination for efficient n: M sparsity. Advances in Neural Information Processing Systems 35, 941--953.
[54]
Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. 2022. COSTA: covariance-preserving feature augmentation for graph contrastive learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery andData Mining. 2524--2534.
[55]
Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. 2023. Spectral feature augmentation for graph contrastive learning and beyond. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 11289--11297.
[56]
Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, and Philip S. Yu. 2018. Spectral collaborative filtering. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys'18). Association for Computing Machinery, 311--319.
[57]
Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. 2022. Multi-level cross-view contrastive learning for knowledgeaware recommender system. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1358--1368.
[58]
Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, and Dawei Yin. 2020. Neural interactive collaborative filtering. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 749--758.

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  1. Self-derived Knowledge Graph Contrastive Learning for Recommendation

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    cover image ACM Conferences
    MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
    October 2024
    11719 pages
    ISBN:9798400706868
    DOI:10.1145/3664647
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    Published: 28 October 2024

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    Author Tags

    1. contrastive learning
    2. knowledge graph
    3. recommendation system

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    MM '24: The 32nd ACM International Conference on Multimedia
    October 28 - November 1, 2024
    Melbourne VIC, Australia

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    MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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