{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:50:18Z","timestamp":1770493818219,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2020YFB1707900"],"award-info":[{"award-number":["2020YFB1707900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62262074"],"award-info":[{"award-number":["62262074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U2268204"],"award-info":[{"award-number":["U2268204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Science and Technology Project of Sichuan Province","award":["2022YFG0159"],"award-info":[{"award-number":["2022YFG0159"]}]},{"name":"the Science and Technology Project of Sichuan Province","award":["2022YFG0155"],"award-info":[{"award-number":["2022YFG0155"]}]},{"name":"the Science and Technology Project of Sichuan Province","award":["2022YFG0157"],"award-info":[{"award-number":["2022YFG0157"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10489-023-04787-y","type":"journal-article","created":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T08:01:26Z","timestamp":1691827286000},"page":"25836-25849","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Community-aware graph contrastive learning for collaborative filtering"],"prefix":"10.1007","volume":"53","author":[{"given":"Dexuan","family":"Lin","sequence":"first","affiliation":[]},{"given":"Xuefeng","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Dasha","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yuming","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"key":"4787_CR1","doi-asserted-by":"crossref","unstructured":"Koren Y, Rendle S, Bell R (2021) Advances in collaborative filtering. Recommender systems handbook 91\u2013142","DOI":"10.1007\/978-1-0716-2197-4_3"},{"key":"4787_CR2","unstructured":"Berg Rvd, Kipf TN, Welling M (2017) Graph convolutional matrix com- pletion. arXiv preprint arXiv:1706.02263"},{"key":"4787_CR3","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender sys-tems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"key":"4787_CR4","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: Simplifying and powering graph convolution network for recommenda-tion. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"4787_CR5","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collab- orative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"4787_CR6","doi-asserted-by":"crossref","unstructured":"Wang Q, Yin H, Wang H, Nguyen QVH, Huang Z, Cui L (2019) Enhancing collaborative filtering with generative augmentation. In: Pro-ceedings of the 25th ACM SIGKDD International Conference on Knowl- edge Discovery & Data Mining, pp. 548\u2013556","DOI":"10.1145\/3292500.3330873"},{"key":"4787_CR7","unstructured":"Borgs C, Chayes J, Lee CE, Shah D (2017) Thy friend is my friend: Iter-ative collaborative filtering for sparse matrix estimation. Advances in neural information processing systems 30"},{"key":"4787_CR8","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G A (2020) simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR"},{"key":"4787_CR9","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"4787_CR10","doi-asserted-by":"crossref","unstructured":"Gao T, Yao X, Chen D (2021) SimCSE: Simple contrastive learning of sen-tence embeddings. In: EmpiricalMethods in Natural Language Processing (EMNLP)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"4787_CR11","doi-asserted-by":"crossref","unstructured":"Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X (2021) Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726\u2013735","DOI":"10.1145\/3404835.3462862"},{"issue":"3","key":"4787_CR12","first-page":"4","volume":"2","author":"P Velickovic","year":"2019","unstructured":"Velickovic P, Fedus W, Hamilton WL, Li\u00f3 P, Bengio Y, Hjelm RD (2019) Deep graph infomax. ICLR (Poster) 2(3):4","journal-title":"Deep graph infomax. ICLR (Poster)"},{"key":"4787_CR13","unstructured":"Ren Y, Zhang J (2021) Collaborative graph contrastive learning: Data aug-mentation composition may not be necessary for graph representation learning. arXiv preprint arXiv:2111.03262"},{"key":"4787_CR14","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Xia X, Chen T, Cui L, Nguyen QVH (2022) Are graph augmentations necessary? simple graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1294\u20131303","DOI":"10.1145\/3477495.3531937"},{"key":"4787_CR15","unstructured":"You Y, Chen T, Shen Y, Wang Z (2021) Graph contrastive learning automated. In: International Conference on Machine Learning, pp. 12121\u201312132. PMLR"},{"key":"4787_CR16","doi-asserted-by":"crossref","unstructured":"Sun M, Xing J,Wang H, Chen B, Zhou J (2021) Mocl: Data-driven molec-ular fingerprint via knowledge-aware contrastive learning from molecular graph. In: Proceedings of the 27th ACM SIGKDD Conference on Knowl-edge Discovery & Data Mining. KDD \u201921, pp. 3585\u20133594. Association for Computing Machinery, New York, NY, USA","DOI":"10.1145\/3447548.3467186"},{"key":"4787_CR17","unstructured":"You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020) Graph con-trastive learning with augmentations. Advances in Neural Information Processing Systems 33, 5812\u20135823"},{"key":"4787_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108103","volume":"114","author":"M Huang","year":"2022","unstructured":"Huang M, Jiang Q, Qu Q, Chen L, Chen H (2022) Information fusion oriented heterogeneous social network for friend recommendation via community detection. Applied Soft Computing 114:108103","journal-title":"Applied Soft Computing"},{"key":"4787_CR19","doi-asserted-by":"crossref","unstructured":"Karypis G, Kumar V (1995) Multilevel graph partitioning schemes. In: Pro-ceedings of The International Conference on Parallel Processing","DOI":"10.1145\/224170.224229"},{"key":"4787_CR20","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhao H, Shi C (2022) Profiling the design space for graph neural networks based collaborative filtering. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 1109\u20131119","DOI":"10.1145\/3488560.3498520"},{"key":"4787_CR21","unstructured":"Welling M, Kipf TN (2016) Semi-supervised classification with graph convo-lutional networks. In: J. International Conference on Learning Represen- tations (ICLR 2017)"},{"key":"4787_CR22","first-page":"20","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. stat 1050:20","journal-title":"Graph attention networks. stat"},{"key":"4787_CR23","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in neural information processing systems 30"},{"key":"4787_CR24","doi-asserted-by":"crossref","unstructured":"Wang X, Wang R, Shi C, Song G, Li Q (2020) Multi-component graph convolutional collaborative filtering. In: Proceedings of the AAAI Confer-ence on Artificial Intelligence, vol. 34, pp. 6267\u20136274","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"4787_CR25","doi-asserted-by":"crossref","unstructured":"Rendle S, Krichene W, Zhang L, Anderson J (2020) Neural collaborative fil-tering vs. matrix factorization revisited. In: Fourteenth ACM Conference on Recommender Systems, pp. 240\u2013248","DOI":"10.1145\/3383313.3412488"},{"key":"4787_CR26","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collab-orative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"4787_CR27","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr:Bayesian personalized ranking from implicit feedback. UAI \u201909, pp. 452\u2013461. AUAI Press, Arlington, Virginia, USA"},{"key":"4787_CR28","doi-asserted-by":"crossref","unstructured":"Su X, Xue S, Liu F, Wu J, Yang J, Zhou C, Hu W, Paris C, Nepal S, Jin D, et al (2022) A comprehensive survey on community detection with deep learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2021.3137396"},{"key":"4787_CR29","doi-asserted-by":"crossref","unstructured":"Rezaeimehr F, Moradi P, Ahmadian S, Qader NN, Jalili M (2018) Tcars:Time-and community-aware recommendation system. Futur Gener Comput Syst 78, 419\u2013429","DOI":"10.1016\/j.future.2017.04.003"},{"key":"4787_CR30","doi-asserted-by":"crossref","unstructured":"Lin X, Zhang M, Liu Y, Ma S (2019) Enhancing personalized recommen-dation by implicit preference communities modeling. ACM Trans Inf Syst (TOIS) 37(4), 1\u201332","DOI":"10.1145\/3352592"},{"key":"4787_CR31","doi-asserted-by":"crossref","unstructured":"Grossetti Q, Du Mouza C, Travers N (2019) Community-based recom-mendations on twitter: avoiding the filter bubble. In: Web Information Systems Engineering\u2013WISE 2019: 20th International Conference, Hong Kong, China, January 19\u20132022, Proceedings 20, pp. 212\u2013227. Springer","DOI":"10.1007\/978-3-030-34223-4_14"},{"key":"4787_CR32","doi-asserted-by":"crossref","unstructured":"He X, He Z, Song J, Liu Z, Jiang Y-G, Chua T-S (2018) Nais: Neural attentive item similarity model for recommendation. IEEE Trans Knowl Data Eng 30(12), 2354\u20132366","DOI":"10.1109\/TKDE.2018.2831682"},{"key":"4787_CR33","unstructured":"Oord Avd, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748"},{"key":"4787_CR34","doi-asserted-by":"crossref","unstructured":"Harper FM, Konstan JA (2015) The movielens datasets: History and con- text. Acm Trans Interact Intell Syst (tiis) 5(4), 1\u201319","DOI":"10.1145\/2827872"},{"key":"4787_CR35","doi-asserted-by":"crossref","unstructured":"McAuley JJ, Leskovec J (2013) From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 897\u2013908","DOI":"10.1145\/2488388.2488466"},{"key":"4787_CR36","doi-asserted-by":"crossref","unstructured":"Geng X, Zhang H, Bian J, Chua T-S (2015) Learning image and user fea-tures for recommendation in social networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4274\u20134282","DOI":"10.1109\/ICCV.2015.486"},{"key":"4787_CR37","doi-asserted-by":"crossref","unstructured":"Wang X, Jin H, Zhang A, He X, Xu T, Chua T-S (2020) Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1001\u20131010","DOI":"10.1145\/3397271.3401137"},{"key":"4787_CR38","doi-asserted-by":"crossref","unstructured":"Mao K, Zhu J, Wang J, Dai Q, Dong Z, Xiao X, He X (2021) Simplex:A simple and strong baseline for collaborative filtering. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1243\u20131252","DOI":"10.1145\/3459637.3482297"},{"key":"4787_CR39","doi-asserted-by":"crossref","unstructured":"Kong T, Kim T, Jeon J, Choi J, Lee Y-C, Park N, Kim S-W (2021) Linear, or Non-Linear, That is the Question!","DOI":"10.1145\/3488560.3498501"},{"key":"4787_CR40","doi-asserted-by":"crossref","unstructured":"Wu L, He X, Wang X, Zhang K, Wang M (2022) A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"4787_CR41","doi-asserted-by":"crossref","unstructured":"Rendle S, Krichene W, Zhang L, Anderson J (2020) Neural collaborative filtering vs. matrix factorization revisited. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp. 240\u2013248","DOI":"10.1145\/3383313.3412488"},{"key":"4787_CR42","unstructured":"Zhang A, Ma W, Wang X, Chua T-S (2023) Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering"},{"key":"4787_CR43","doi-asserted-by":"crossref","unstructured":"Yang C, Wu Q, Jin J, Gao X, Pan J, Chen G (2022) Trading Hard Nega-tives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach","DOI":"10.24963\/ijcai.2022\/327"},{"key":"4787_CR44","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M,Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"4787_CR45","doi-asserted-by":"crossref","unstructured":"Liu Y, Jin M, Pan S, Zhou C, Zheng Y, Xia F, Yu P (2022) Graph self-supervised learning: A survey. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2022.3172903"},{"key":"4787_CR46","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Deep Graph Contrastive Representation Learning. In: ICML Workshop on Graph Representation Learning and Beyond"},{"key":"4787_CR47","doi-asserted-by":"crossref","unstructured":"Xia L, Huang C, Xu Y, Zhao J, Yin D, Huang J (2022) Hypergraph con-trastive collaborative filtering. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","DOI":"10.1145\/3477495.3532058"},{"key":"4787_CR48","unstructured":"Verma V, Luong T, Kawaguchi K, Pham H, Le Q (2021) Towards domain-agnostic contrastive learning. In: International Conference on Machine Learning, pp. 10530\u201310541. PMLR"},{"key":"4787_CR49","doi-asserted-by":"crossref","unstructured":"Huang T, Dong Y, Ding M, Yang Z, Feng W, Wang X, Tang J (2021) Mixgcf: An improved training method for graph neural network-based recommender systems. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 665\u2013674","DOI":"10.1145\/3447548.3467408"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04787-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04787-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04787-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T14:25:26Z","timestamp":1698071126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04787-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,12]]},"references-count":49,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4787"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04787-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,12]]},"assertion":[{"value":"10 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors give consent to participate.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors give consent for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.The authors have no financial or proprietary interests in any material discussed in this article.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}