{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:22:19Z","timestamp":1772749339647,"version":"3.50.1"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key Research and Development Program of Jiangsu Province","award":["BE2023016-4"],"award-info":[{"award-number":["BE2023016-4"]}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61936005, 62325206, and 72074038"],"award-info":[{"award-number":["61936005, 62325206, and 72074038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK.20210595"],"award-info":[{"award-number":["BK.20210595"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX23_1026"],"award-info":[{"award-number":["KYCX23_1026"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Graph Collaborative Filtering is a widely adopted approach for recommendation, which captures similar behavior features through Graph Neural Network (GNN). Recently, Contrastive Learning (CL) has been demonstrated as an effective method to enhance the performance of graph collaborative filtering. Typically, CL-based methods first perturb users\u2019 history behavior data (e.g., drop clicked items), then construct a self-discriminating task for behavior representations under different random perturbations. However, for widely existing inactive users, random perturbation makes their sparse behavior information more incomplete, thereby harming the behavior feature extraction. To tackle the above issue, we design a novel directional perturbation-based CL method to improve the graph collaborative filtering performance. The idea is to perturb node representations through directionally enhancing behavior features. To do so, we propose a simple yet effective feedback mechanism, which fuses the representations of nodes based on behavior similarity. Then, to avoid irrelevant behavior preferences introduced by the feedback mechanism, we construct a behavior self-contrast task before and after feedback, to align the node representations between the final output and the first layer of GNN. Different from the widely adopted self-discriminating task, the behavior self-contrast task avoids complex message propagation on different perturbed graphs, which is more efficient than previous methods. Extensive experiments on three public datasets demonstrate that the proposed method has distinct advantages over other CL methods on recommendation accuracy.<\/jats:p>","DOI":"10.1145\/3663574","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T18:44:06Z","timestamp":1714675446000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Graph Collaborative Filtering with Directional Behavior Enhanced Contrastive Learning"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3233-4248","authenticated-orcid":false,"given":"Penghang","family":"Yu","sequence":"first","affiliation":[{"name":"University of Posts and Telecommunications, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5956-831X","authenticated-orcid":false,"given":"Bing-Kun","family":"Bao","sequence":"additional","affiliation":[{"name":"University of Posts and Telecommunications, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-2817","authenticated-orcid":false,"given":"Zhiyi","family":"Tan","sequence":"additional","affiliation":[{"name":"University of Posts and Telecommunications, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4860-8229","authenticated-orcid":false,"given":"Guanming","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Posts and Telecommunications, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367618"},{"key":"e_1_3_1_3_2","unstructured":"Raja Muhammad Saad Bashir Talha Qaiser Shan E. Ahmed Raza and Nasir M. Rajpoot. 2023. Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images. arXiv:2301.13141."},{"key":"e_1_3_1_4_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. Simple yet effective graph contrastive learning for recommendation. In Proceedings of the International Conference on Learning Representations. OpenReview.net"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_1_7_2","first-page":"898","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"30","author":"Dai Bo","year":"2017","unstructured":"Bo Dai and Dahua Lin. 2017. Contrastive learning for image captioning. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30, 898\u2013907"},{"key":"e_1_3_1_8_2","unstructured":"Tianyu Gao Xingcheng Yao and Danqi Chen. 2021. SimCSE: Simple contrastive learning of sentence embeddings. arXiv:2104.08821."},{"key":"e_1_3_1_9_2","first-page":"21271","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"33","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R\u00e9mi Munos, and Michal Valko. 2020. Bootstrap your own latent-a new approach to self-supervised learning. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33. 21271\u201321284."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_1_14_2","unstructured":"Taewook Ko Yoonhyuk Choi and Chong-Kwon Kim. 2023. Signed directed graph contrastive learning with Laplacian augmentation. arXiv:2301.05163."},{"key":"e_1_3_1_15_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Komodakis Nikos","year":"2018","unstructured":"Nikos Komodakis and Spyros Gidaris. 2018. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88013-2_8"},{"key":"e_1_3_1_18_2","unstructured":"Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv:1909.11942."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462935"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512104"},{"key":"e_1_3_1_21_2","unstructured":"Zhuang Liu Yunpu Ma Yuanxin Ouyang and Zhang Xiong. 2021. Contrastive learning for recommender system. arXiv:2101.01317."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482291"},{"key":"e_1_3_1_23_2","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv:1807.03748."},{"key":"e_1_3_1_24_2","first-page":"452","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452\u2013461."},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/978-3-540-72079-9_9","volume-title":"The Adaptive Web: Methods and Strategies of Web Personalization","author":"Ben Schafer J.","year":"2007","unstructured":"J. Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The Adaptive Web: Methods and Strategies of Web Personalization. Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.), Springer, Berlin Heidelberg, 291\u2013324."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2017.72"},{"issue":"2009","key":"e_1_3_1_27_2","first-page":"421425","article-title":"A survey of collaborative filtering techniques","volume":"2009","author":"Su Xiaoyuan","year":"2009","unstructured":"Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009 (2009), 421425.","journal-title":"Advances in Artificial Intelligence"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176348768"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531889"},{"key":"e_1_3_1_30_2","first-page":"19580","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"34","author":"Tong Zekun","year":"2021","unstructured":"Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang. 2021. Directed graph contrastive learning. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34. 19580\u201319593."},{"key":"e_1_3_1_31_2","first-page":"11","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten Laurens","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539253"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557317"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"e_1_3_1_38_2","first-page":"6861","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of the International Conference on Machine Learning. PMLR, 6861\u20136871."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450015"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000096"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"issue":"4","key":"e_1_3_1_44_2","first-page":"1","article-title":"On the vulnerability of graph learning based collaborative filtering","volume":"41","author":"Xu Senrong","year":"2022","unstructured":"Senrong Xu, Liangyue Li, Zenan Li, Yuan Yao, Feng Xu, Zulong Chen, Quan Lu, and Hanghang Tong. 2022. On the vulnerability of graph learning based collaborative filtering. ACM Transactions on Information Systems 41, 4 (2023), 1\u201328.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240381"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_1_47_2","unstructured":"Junliang Yu Xin Xia Tong Chen Lizhen Cui Nguyen Quoc Viet Hung and Hongzhi Yin. 2022a. XSimGCL: Towards extremely simple graph contrastive learning for recommendation. arXiv:2209.02544."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449844"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"key":"e_1_3_1_50_2","unstructured":"Junliang Yu Hongzhi Yin Xin Xia Tong Chen Jundong Li and Zi Huang. 2022c. Self-supervised learning for recommender systems: A survey. arXiv:2203.15876."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482426"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3591469"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663574","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3663574","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:57:59Z","timestamp":1750294679000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":52,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3663574"],"URL":"https:\/\/doi.org\/10.1145\/3663574","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"2023-04-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-29","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}