{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:59:11Z","timestamp":1776697151427,"version":"3.51.2"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031306716","type":"print"},{"value":"9783031306723","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30672-3_25","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T11:10:49Z","timestamp":1681384249000},"page":"373-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Adversarial Learning Data Augmentation for\u00a0Graph Contrastive Learning in\u00a0Recommendation"],"prefix":"10.1007","author":[{"given":"Junjie","family":"Huang","sequence":"first","affiliation":[]},{"given":"Qi","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Ruobing","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Shaoliang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Huawei","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"25_CR1","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597\u20131607. PMLR (2020)"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: POG: personalized outfit generation for fashion recommendation at alibaba ifashion. In: KDD, pp. 2662\u20132670 (2019)","DOI":"10.1145\/3292500.3330652"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: KDD, pp. 1082\u20131090 (2011)","DOI":"10.1145\/2020408.2020579"},{"key":"25_CR4","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: ICML, pp. 1263\u20131272. PMLR (2017)"},{"key":"25_CR5","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NeurIPS 27 (2014)"},{"key":"25_CR6","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)"},{"key":"25_CR7","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: AISTATS, pp. 297\u2013304 (2010)"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639\u2013648 (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"25_CR10","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: ICLR (2017)"},{"key":"25_CR11","unstructured":"Jin, W., et al.: Self-supervised learning on graphs: deep insights and new direction. arXiv preprint arXiv:2006.10141 (2020)"},{"key":"25_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"issue":"8","key":"25_CR13","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.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: WWW, pp. 689\u2013698 (2018)","DOI":"10.1145\/3178876.3186150"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: WWW, pp. 2320\u20132329 (2022)","DOI":"10.1145\/3485447.3512104"},{"key":"25_CR16","unstructured":"Luo, D., et al.: Parameterized explainer for graph neural network. In: NeurIPS 33, pp. 19620\u201319631 (2020)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR, pp. 43\u201352 (2015)","DOI":"10.1145\/2766462.2767755"},{"key":"25_CR18","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: RecSys, pp. 240\u2013248 (2020)","DOI":"10.1145\/3383313.3412488"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285\u2013295 (2001)","DOI":"10.1145\/371920.372071"},{"key":"25_CR21","unstructured":"Suresh, S., Li, P., Hao, C., Neville, J.: Adversarial graph augmentation to improve graph contrastive learning. In: NeurIPS 34 (2021)"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Tao, S., Cao, Q., Shen, H., Huang, J., Wu, Y., Cheng, X.: Single node injection attack against graph neural networks. In: CIKM, pp. 1794\u20131803 (2021)","DOI":"10.1145\/3459637.3482393"},{"key":"25_CR23","unstructured":"Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., Isola, P.: What makes for good views for contrastive learning? In NeurIPS 33, pp. 6827\u20136839 (2020)"},{"key":"25_CR24","unstructured":"Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method. arXiv preprint physics\/0004057 (2000)"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: SIGIR, pp. 165\u2013174 (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.S.: Disentangled graph collaborative filtering. In: SIGIR, pp. 1001\u20131010 (2020)","DOI":"10.1145\/3397271.3401137"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Self-supervised graph learning for recommendation. In: SIGIR, pp. 726\u2013735 (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"25_CR28","unstructured":"Wu, L., Lin, H., Tan, C., Gao, Z., Li, S.Z.: Self-supervised learning on graphs: contrastive, generative, or predictive. IEEE TKDE (2021)"},{"key":"25_CR29","unstructured":"Xu, D., Cheng, W., Luo, D., Chen, H., Zhang, X.: InfoGCL: information-aware graph contrastive learning. In: NeurIPS 34 (2021)"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Xue, H.J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, Melbourne, Australia, vol. 17, pp. 3203\u20133209 (2017)","DOI":"10.24963\/ijcai.2017\/447"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Yao, T., et al.: Self-supervised learning for large-scale item recommendations. In: CIKM, pp. 4321\u20134330 (2021)","DOI":"10.1145\/3459637.3481952"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Li, J., Wang, Q., Hung, N.Q.V., Zhang, X.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: WWW, pp. 413\u2013424 (2021)","DOI":"10.1145\/3442381.3449844"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., Nguyen, Q.V.H.: Are graph augmentations necessary? simple graph contrastive learning for recommendation. In: SIGIR, pp. 1294\u20131303 (2022)","DOI":"10.1145\/3477495.3531937"},{"key":"25_CR34","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Li, J., Huang, Z.: Self-supervised learning for recommender systems: a survey. arXiv preprint arXiv:2203.15876 (2022)"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, W.X., et al.: RecBole: towards a unified, comprehensive and efficient framework for recommendation algorithms. In: CIKM, pp. 4653\u20134664 (2021)","DOI":"10.1145\/3459637.3482016"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30672-3_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:28:25Z","timestamp":1710264505000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30672-3_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306716","9783031306723"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30672-3_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}