{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:04:25Z","timestamp":1780945465328,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10618-024-01038-7","type":"journal-article","created":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T15:02:56Z","timestamp":1717340576000},"page":"2440-2465","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving graph-based recommendation with unraveled graph learning"],"prefix":"10.1007","volume":"38","author":[{"given":"Chih-Chieh","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diing-Ruey","family":"Tzeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Hsun","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Yi","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Ya","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,2]]},"reference":[{"key":"1038_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.11648\/j.ijhep.20210801.11","volume":"8","author":"O Atkinson","year":"2021","unstructured":"Atkinson O, Bhardwaj A, Englert C et al (2021) Anomaly detection with convolutional graph neural networks. J High Energy Phys 8:1\u201319","journal-title":"J High Energy Phys"},{"issue":"15s","key":"1038_CR2","first-page":"644","volume":"12","author":"VV Bag","year":"2024","unstructured":"Bag VV, Patil MB, Gaikwad VD et al (2024) Revolutionizing fashion: Fashion era\u2019s deep convolutional neural network for outfit recommendations. Int J Intell Syst Appl Eng 12(15s):644\u2013650","journal-title":"Int J Intell Syst Appl Eng"},{"key":"1038_CR3","unstructured":"Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263"},{"issue":"5","key":"1038_CR4","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1109\/TCSS.2022.3152179","volume":"9","author":"CC Chang","year":"2022","unstructured":"Chang CC, Chang MY, Jhang JY et al (2022) Learning to extract expert teams in social networks. IEEE Trans Comput Soc Syst 9(5):1552\u20131562","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"1038_CR5","doi-asserted-by":"crossref","unstructured":"Chen L, Wu L, Hong R, et\u00a0al (2020) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI conference on artificial intelligence, pp 27\u201334","DOI":"10.1609\/aaai.v34i01.5330"},{"issue":"12","key":"1038_CR6","doi-asserted-by":"publisher","first-page":"2364","DOI":"10.1109\/TKDE.2018.2875911","volume":"31","author":"YL Chen","year":"2018","unstructured":"Chen YL, Yang DN, Shen CY et al (2018) On efficient processing of group and subsequent queries for social activity planning. IEEE Trans Knowl Data Eng 31(12):2364\u20132378","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1038_CR7","doi-asserted-by":"crossref","unstructured":"Ge Y, Liu S, Gao R, et\u00a0al (2021) Towards long-term fairness in recommendation. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 445\u2013453","DOI":"10.1145\/3437963.3441824"},{"issue":"4","key":"1038_CR8","first-page":"1","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper FM, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst (TIIS) 5(4):1\u201319","journal-title":"ACM Trans Interact Intell Syst (TIIS)"},{"key":"1038_CR9","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, et\u00a0al (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"1038_CR10","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, et\u00a0al (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, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"1038_CR11","doi-asserted-by":"crossref","unstructured":"Hsu BY, Shen CY, Chang MY (2020) Wmego: willingness maximization for ego network data extraction in online social networks. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 515\u2013524","DOI":"10.1145\/3340531.3411867"},{"key":"1038_CR12","doi-asserted-by":"crossref","unstructured":"Huang T, Dong Y, Ding M, et\u00a0al (2021) Mixgcf: an improved training method for graph neural network-based recommender systems. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining, pp 665\u2013674","DOI":"10.1145\/3447548.3467408"},{"key":"1038_CR13","doi-asserted-by":"crossref","unstructured":"Huang YL, Shen CY, Shieh S, et\u00a0al (2009) Provable secure aka scheme with reliable key delegation in umts. In: 2009 Third IEEE international conference on secure software integration and reliability improvement, IEEE, pp 243\u2013252","DOI":"10.1109\/SSIRI.2009.62"},{"key":"1038_CR14","doi-asserted-by":"crossref","unstructured":"Jiang W, Luo J (2022) Graph neural network for traffic forecasting: a survey. Expert Syst Appl, 117921","DOI":"10.1016\/j.eswa.2022.117921"},{"issue":"8","key":"1038_CR15","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes S, McCloskey K, Berndl M et al (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30(8):595\u2013608","journal-title":"J Comput Aided Mol Des"},{"key":"1038_CR16","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: ICLR (Poster)"},{"issue":"8","key":"1038_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"1038_CR18","doi-asserted-by":"crossref","unstructured":"Lee D, Kang S, Ju H, et\u00a0al (2021) Bootstrapping user and item representations for one-class collaborative filtering. In: Proceedings of the 44th International ACM SIGIR conference on research and development in information retrieval, pp 317\u2013326","DOI":"10.1145\/3404835.3462935"},{"key":"1038_CR19","doi-asserted-by":"crossref","unstructured":"Liang D, Krishnan RG, Hoffman MD, et\u00a0al (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference, pp 689\u2013698","DOI":"10.1145\/3178876.3186150"},{"key":"1038_CR20","first-page":"2320","volume":"2022","author":"Z Lin","year":"2022","unstructured":"Lin Z, Tian C, Hou Y et al (2022) Improving graph collaborative filtering with neighborhood-enriched contrastive learning. Proc ACM Web Conf 2022:2320\u20132329","journal-title":"Proc ACM Web Conf"},{"key":"1038_CR21","doi-asserted-by":"crossref","unstructured":"Lin Z, Tian C, Hou Y, et\u00a0al (2022b) Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: WWW","DOI":"10.1145\/3485447.3512104"},{"key":"1038_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2022.101695","volume":"62","author":"SK Maurya","year":"2022","unstructured":"Maurya SK, Liu X, Murata T (2022) Simplifying approach to node classification in graph neural networks. J Comput Sci 62:101695","journal-title":"J Comput Sci"},{"key":"1038_CR23","unstructured":"Pan L, Shi C, Dokmani\u0107 I (2021) Neural link prediction with walk pooling. arXiv preprint arXiv:2110.04375"},{"key":"1038_CR24","unstructured":"Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, pp 5\u20138"},{"key":"1038_CR25","unstructured":"Rendle S, Freudenthaler C, Gantner Z, et\u00a0al (2012) BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618"},{"key":"1038_CR26","unstructured":"Shchur O, Mumme M, Bojchevski A, et\u00a0al (2018) Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868"},{"issue":"1","key":"1038_CR27","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TKDE.2020.2980516","volume":"34","author":"CY Shen","year":"2020","unstructured":"Shen CY, Yang DN, Lee WC et al (2020) Activity organization for friend-making optimization in online social networks. IEEE Trans Knowl Data Eng 34(1):122\u2013137","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1038_CR28","doi-asserted-by":"crossref","unstructured":"Shuai HH, Yang DN, Philip SY, et\u00a0al (2013) On pattern preserving graph generation. In: 2013 IEEE 13th international conference on data mining, IEEE, pp 677\u2013686","DOI":"10.1109\/ICDM.2013.14"},{"key":"1038_CR29","doi-asserted-by":"crossref","unstructured":"Sun J, Zhang Y, Ma C, et\u00a0al (2019) Multi-graph convolution collaborative filtering. In: 2019 IEEE international conference on data mining (ICDM), IEEE, pp 1306\u20131311","DOI":"10.1109\/ICDM.2019.00165"},{"key":"1038_CR30","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et\u00a0al (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"1038_CR31","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, et\u00a0al (2019) Neural graph collaborative 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":"1038_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Jin H, Zhang A, et\u00a0al (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":"1038_CR33","unstructured":"Wu F, Souza A, Zhang T, et\u00a0al (2019) Simplifying graph convolutional networks. In: International conference on machine learning, PMLR, pp 6861\u20136871"},{"key":"1038_CR34","doi-asserted-by":"crossref","unstructured":"Wu J, Wang X, Feng F, et\u00a0al (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"},{"key":"1038_CR35","doi-asserted-by":"crossref","unstructured":"Yang CH, Shen CY (2022) Enhancing machine learning approaches for graph optimization problems with diversifying graph augmentation. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 2191\u20132201","DOI":"10.1145\/3534678.3539437"},{"key":"1038_CR36","doi-asserted-by":"crossref","unstructured":"Yao T, Yi X, Cheng DZ, et\u00a0al (2021) Self-supervised learning for large-scale item recommendations. In: Proceedings of the 30th ACM international conference on information and knowledge management, pp 4321\u20134330","DOI":"10.1145\/3459637.3481952"},{"key":"1038_CR37","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Gao M, et\u00a0al (2021a) Socially-aware self-supervised tri-training for recommendation. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining, pp 2084\u20132092","DOI":"10.1145\/3447548.3467340"},{"key":"1038_CR38","first-page":"413","volume":"2021","author":"J Yu","year":"2021","unstructured":"Yu J, Yin H, Li J et al (2021) Self-supervised multi-channel hypergraph convolutional network for social recommendation. Proc Web Conf 2021:413\u2013424","journal-title":"Proc Web Conf"},{"key":"1038_CR39","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Xia X, et\u00a0al (2022a) 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":"1038_CR40","unstructured":"Yu J, Yin H, Xia X, et\u00a0al (2022b) Self-supervised learning for recommender systems: a survey. arXiv preprint arXiv:2203.15876"},{"key":"1038_CR41","doi-asserted-by":"crossref","unstructured":"Zhao WX, Mu S, Hou Y, et\u00a0al (2021) Recbole: towards a unified, comprehensive and efficient framework for recommendation algorithms. In: CIKM","DOI":"10.1145\/3459637.3482016"},{"key":"1038_CR42","doi-asserted-by":"crossref","unstructured":"Zheng L, Lu CT, Jiang F, et\u00a0al (2018) Spectral collaborative filtering. In: Proceedings of the 12th ACM conference on recommender systems, pp 311\u2013319","DOI":"10.1145\/3240323.3240343"},{"key":"1038_CR43","first-page":"2069","volume":"2021","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Xu Y, Yu F et al (2021) Graph contrastive learning with adaptive augmentation. Proc Web Conf 2021:2069\u20132080","journal-title":"Proc Web Conf"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01038-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-024-01038-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01038-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T10:03:17Z","timestamp":1722333797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-024-01038-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,2]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["1038"],"URL":"https:\/\/doi.org\/10.1007\/s10618-024-01038-7","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,2]]},"assertion":[{"value":"5 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}