{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:47:21Z","timestamp":1777621641393,"version":"3.51.4"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376180"],"award-info":[{"award-number":["62376180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2023YFF0725000"],"award-info":[{"award-number":["2023YFF0725000"]}]},{"name":"Universities of Jiangsu Province","award":["21KJA520004"],"award-info":[{"award-number":["21KJA520004"]}]},{"name":"Suzhou Science and Technology Development Program","award":["SYG202328"],"award-info":[{"award-number":["SYG202328"]}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Graph collaborative filtering (GCF) has achieved exciting recommendation performance with its ability to aggregate high-order graph structure information. Recently, contrastive learning (CL) has been incorporated into GCF to alleviate data sparsity and noise issues. However, most of the existing methods employ random or manual augmentation to produce contrastive views that may destroy the original topology and amplify the noisy effects. We argue that such augmentation is insufficient to produce the optimal contrastive view, leading to suboptimal recommendation results. In this article, we proposed a\n            <jats:bold>L<\/jats:bold>\n            earnable\n            <jats:bold>M<\/jats:bold>\n            odel\n            <jats:bold>A<\/jats:bold>\n            ugmentation\n            <jats:bold>C<\/jats:bold>\n            ontrastive\n            <jats:bold>L<\/jats:bold>\n            earning (LMACL) framework for recommendation, which effectively combines graph-level and node-level collaborative relations to enhance the expressiveness of collaborative filtering (CF) paradigm. Specifically, we first use the graph convolution network (GCN) as a backbone encoder to incorporate multi-hop neighbors into graph-level original node representations by leveraging the high-order connectivity in user-item interaction graphs. At the same time, we treat the multi-head graph attention network (GAT) as an augmentation view generator to adaptively generate high-quality node-level augmented views. Finally, joint learning endows the end-to-end training fashion. In this case, the mutual supervision and collaborative cooperation of GCN and GAT achieves learnable model augmentation. Extensive experiments on several benchmark datasets demonstrate that LMACL provides a significant improvement over the strongest baseline in terms of\n            <jats:italic>Recall<\/jats:italic>\n            and\n            <jats:italic>NDCG<\/jats:italic>\n            by 2.5%\u20133.8% and 1.6%\u20134.0%, respectively. Our model implementation code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/LiuHsinx\/LMACL\">https:\/\/github.com\/LiuHsinx\/LMACL<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3657302","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T12:25:34Z","timestamp":1712924734000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0190-6066","authenticated-orcid":false,"given":"Xinru","family":"Liu","sequence":"first","affiliation":[{"name":"Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5063-7307","authenticated-orcid":false,"given":"Yongjing","family":"Hao","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5123-9279","authenticated-orcid":false,"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8980-4950","authenticated-orcid":false,"given":"Guanfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4960-174X","authenticated-orcid":false,"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Texas Tech University, Lubbock United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6721-6576","authenticated-orcid":false,"given":"Pengpeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29659-3_3"},{"key":"e_1_3_1_3_2","article-title":"Relational graph attention networks","author":"Busbridge Dan","year":"2019","unstructured":"Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y. Hammerla. 2019. Relational graph attention networks. arXiv preprint arXiv:1904.05811 (2019).","journal-title":"arXiv preprint arXiv:1904.05811"},{"key":"e_1_3_1_4_2","article-title":"LightGCL: Simple yet effective graph contrastive learning for recommendation","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. arXiv preprint arXiv:2302.08191 (2023).","journal-title":"arXiv preprint arXiv:2302.08191"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080797"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_1_9_2","first-page":"173","volume-title":"WWW","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173\u2013182."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-30672-3_25"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_1_12_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_1_14_2","article-title":"Graph transformer for recommendation","author":"Li Chaoliu","year":"2023","unstructured":"Chaoliu Li, Lianghao Xia, Xubin Ren, Yaowen Ye, Yong Xu, and Chao Huang. 2023. Graph transformer for recommendation. arXiv preprint arXiv:2306.02330 (2023).","journal-title":"arXiv preprint arXiv:2306.02330"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806527"},{"key":"e_1_3_1_16_2","first-page":"626","volume-title":"Big Data","author":"Li Xiaohan","year":"2022","unstructured":"Xiaohan Li, Yuqing Liu, Zheng Liu, and Philip S. Yu. 2022. Time-aware hyperbolic graph attention network for session-based recommendation. In Big Data. IEEE, 626\u2013635."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671830"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00041"},{"issue":"6","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3577928","article-title":"Edge-enhanced global disentangled graph neural network for sequential recommendation","volume":"17","author":"Li Yunyi","year":"2023","unstructured":"Yunyi Li, Yongjing Hao, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, and Xiaofang Zhou. 2023. Edge-enhanced global disentangled graph neural network for sequential recommendation. Trans. Knowl. Discov. Data 17, 6 (2023), 1\u201322.","journal-title":"Trans. Knowl. Discov. Data"},{"key":"e_1_3_1_20_2","first-page":"689","volume-title":"WWW","author":"Liang Dawen","year":"2018","unstructured":"Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689\u2013698."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403233"},{"key":"e_1_3_1_22_2","first-page":"2320","volume-title":"WWW","author":"Lin Zihan","year":"2022","unstructured":"Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In WWW. 2320\u20132329."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9377917"},{"key":"e_1_3_1_24_2","article-title":"Representation learning with contrastive predictive coding","author":"Oord Aaron van den","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).","journal-title":"arXiv preprint arXiv:1807.03748"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358010"},{"key":"e_1_3_1_26_2","article-title":"Disentangled contrastive collaborative filtering","author":"Ren Xubin","year":"2023","unstructured":"Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, and Chao Huang. 2023. Disentangled contrastive collaborative filtering. arXiv preprint arXiv:2305.02759 (2023).","journal-title":"arXiv preprint arXiv:2305.02759"},{"key":"e_1_3_1_27_2","article-title":"BPR: Bayesian personalized ranking from implicit feedback","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).","journal-title":"arXiv preprint arXiv:1205.2618"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"e_1_3_1_29_2","article-title":"SiReN: Sign-aware recommendation using graph neural networks","author":"Seo Changwon","year":"2022","unstructured":"Changwon Seo, Kyeong-Joong Jeong, Sungsu Lim, and Won-Yong Shin. 2022. SiReN: Sign-aware recommendation using graph neural networks. TNNLS (2022).","journal-title":"TNNLS"},{"key":"e_1_3_1_30_2","article-title":"Neighbor contrastive learning on learnable graph augmentation","author":"Shen Xiao","year":"2023","unstructured":"Xiao Shen, Dewang Sun, Shirui Pan, Xi Zhou, and Laurence T. Yang. 2023. Neighbor contrastive learning on learnable graph augmentation. arXiv preprint arXiv:2301.01404 (2023).","journal-title":"arXiv preprint arXiv:2301.01404"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290989"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462833"},{"key":"e_1_3_1_33_2","first-page":"593","volume-title":"WWW","author":"Sun Jianing","year":"2021","unstructured":"Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe P\u00e9rez, and Maksims Volkovs. 2021. HGCF: Hyperbolic graph convolution networks for collaborative filtering. In WWW. 593\u2013601."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00165"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411947"},{"key":"e_1_3_1_36_2","article-title":"Link prediction in relational data","author":"Taskar Ben","year":"2003","unstructured":"Ben Taskar, Ming-Fai Wong, Pieter Abbeel, and Daphne Koller. 2003. Link prediction in relational data. In NIPS.","journal-title":"NIPS"},{"issue":"1","key":"e_1_3_1_37_2","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","author":"Maaten Laurens Van Der","year":"2014","unstructured":"Laurens Van Der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 1 (2014), 3221\u20133245.","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"e_1_3_1_38_2","article-title":"Visualizing data using t-SNE.","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 11 (2008).","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_1_39_2","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).","journal-title":"arXiv preprint arXiv:1710.10903"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_1_42_2","first-page":"2022","volume-title":"WWW","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous graph attention network. In WWW. 2022\u20132032."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.61"},{"key":"e_1_3_1_45_2","first-page":"20407","article-title":"Contrastive graph structure learning via information bottleneck for recommendation","author":"Wei Chunyu","year":"2022","unstructured":"Chunyu Wei, Jian Liang, Di Liu, and Fei Wang. 2022. Contrastive graph structure learning via information bottleneck for recommendation. In NIPS. 20407\u201320420.","journal-title":"NIPS"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_1_47_2","first-page":"992","volume-title":"WWW","author":"Xia Lianghao","year":"2023","unstructured":"Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, and Ben Kao. 2023. Automated self-supervised learning for recommendation. In WWW. 992\u20131002."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532058"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539473"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482388"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.08.021"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599400"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532009"},{"key":"e_1_3_1_55_2","doi-asserted-by":"crossref","unstructured":"Y. Yang Z. Wu L. Wu et\u00a0al. 2023. Generative-contrastive graph learning for recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1117\u20131126.","DOI":"10.1145\/3539618.3591691"},{"key":"e_1_3_1_56_2","unstructured":"Tiansheng Yao Xinyang Yi Derek Zhiyuan Cheng Felix Yu Ting Chen Aditya Menon Lichan Hong Ed H. Chi Steve Tjoa Jieqi Kang and others. 2021. Self-supervised learning for large-scale item recommendations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4321\u20134330."},{"key":"e_1_3_1_57_2","first-page":"1294","volume-title":"SIGIR","author":"Yu Junliang","year":"2022","unstructured":"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\u20131303."},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412095"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411954"},{"key":"e_1_3_1_60_2","unstructured":"Hanwen Du Huanhuan Yuan Pengpeng Zhao Deqing Wang Victor S. Sheng Yanchi Liu Guanfeng Liu and Lei Zhao. 2023. Feature-aware contrastive learning with bidirectional transformers for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_1_61_2","unstructured":"Yongjing Hao Tingting Zhang Pengpeng Zhao Yanchi Liu Victor S. Sheng Jiajie Xu Guanfeng Liu and Xiaofang Zhou. 2023. Feature-level deeper self-attention network with contrastive learning for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering (2023)."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3657302","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3657302","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:39Z","timestamp":1750295859000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3657302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":60,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,8,31]]}},"alternative-id":["10.1145\/3657302"],"URL":"https:\/\/doi.org\/10.1145\/3657302","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,19]]},"assertion":[{"value":"2023-08-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}