{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:37:01Z","timestamp":1774021021422,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031001253","type":"print"},{"value":"9783031001260","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-00126-0_15","type":"book-chapter","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:07:55Z","timestamp":1650996475000},"page":"232-247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Diffusion-Based Graph Contrastive Learning for\u00a0Recommendation with\u00a0Implicit Feedback"],"prefix":"10.1007","author":[{"given":"Lingzi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Chunyan","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Guoxin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haihong","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Andersen, R., Chung, F., Lang, K.: Local graph partitioning using PageRank vectors. In: FOCS 2006, pp. 475\u2013486 (2006)","DOI":"10.1109\/FOCS.2006.44"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Bojchevski, A., et al.: Scaling graph neural networks with approximate PageRank. In: KDD 2020, pp. 2464\u20132473 (2020)","DOI":"10.1145\/3394486.3403296"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Cao, J., Lin, X., Guo, S., Liu, L., Liu, T., Wang, B.: Bipartite graph embedding via mutual information maximization. In: WSDM 2021, pp. 635\u2013643 (2021)","DOI":"10.1145\/3437963.3441783"},{"key":"15_CR4","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS 2010, pp. 249\u2013256 (2010)"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Han, X., Shi, C., Wang, S., Yu, P.S., Song, L.: Aspect-level deep collaborative filtering via heterogeneous information networks. In: IJCAI 2018, pp. 3393\u20133399 (2018)","DOI":"10.24963\/ijcai.2018\/471"},{"key":"15_CR6","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: ICML 2020, pp. 4116\u20134126 (2020)"},{"key":"15_CR7","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 2020 (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Liu, H., Fu, B., Wu, Z., Zhang, T.: Recommendation in heterogeneous information networks based on generalized random walk model and Bayesian personalized ranking. In: WSDM 2018, pp. 288\u2013296 (2018)","DOI":"10.1145\/3159652.3159715"},{"key":"15_CR9","unstructured":"Jin, W., et al.: Self-supervised learning on graphs: deep insights and new direction. arXiv preprint arXiv:2006.10141 (2020)"},{"key":"15_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"15_CR11","unstructured":"Klicpera, J., Bojchevski, A., G\u00fcnnemann, S.: Predict then propagate: graph neural networks meet personalized PageRank. arXiv:1810.05997 (2018)"},{"key":"15_CR12","unstructured":"Klicpera, J., Wei\u00dfenberger, S., G\u00fcnnemann, S.: Diffusion improves graph learning. In: NeurIPS 2019 (2019)"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Lee, D., Kang, S., Ju, H., Park, C., Yu, H.: Bootstrapping user and item representations for one-class collaborative filtering. In: SIGIR 2021, pp. 317\u2013326 (2021)","DOI":"10.1145\/3404835.3462935"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Lei, C., et al.: SEMI: a sequential multi-modal information transfer network for e-commerce micro-video recommendations. In: KDD 2021, pp. 3161\u20133171 (2021)","DOI":"10.1145\/3447548.3467189"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Pre-training graph transformer with multimodal side information for recommendation. In: MM 2021, pp. 2853\u20132861 (2021)","DOI":"10.1145\/3474085.3475709"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, S., Xu, Y., Miao, C., Wu, M., Zhang, J.: Contextualized graph attention network for recommendation with item knowledge graph. TKDE (2021)","DOI":"10.1109\/TKDE.2021.3082948"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, S., Zhang, Y., Miao, C., Nie, Z., Zhang, J.: Learning hierarchical review graph representations for recommendation. TKDE (2021)","DOI":"10.1109\/TKDE.2021.3075052"},{"key":"15_CR18","unstructured":"Liu, Z., Ma, Y., Ouyang, Y., Xiong, Z.: Contrastive learning for recommender system. arXiv:2101.01317 (2021)"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: EMNLP-IJCNLP 2019, pp. 188\u2013197 (2019)","DOI":"10.18653\/v1\/D19-1018"},{"key":"15_CR20","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical report 1999-66 (1999)"},{"key":"15_CR21","unstructured":"Paszke, A., Gross, S., Massa, F., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035 (2019)"},{"key":"15_CR22","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618 (2012)"},{"key":"15_CR23","unstructured":"Kondor, R.I., Lafferty, J.D.: Diffusion kernels on graphs and other discrete input spaces. In: ICML 2002 (2002)"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Tang, H., Zhao, G., Wu, Y., Qian, X.: Multi-sample based contrastive loss for top-k recommendation. arXiv:2109.00217 (2021)","DOI":"10.1109\/TMM.2021.3126146"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: KDD 2019, pp. 950\u2013958 (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Self-supervised graph learning for recommendation. arXiv:2010.10783 (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"15_CR27","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: NeurIPS 2020, vol. 33, pp. 5812\u20135823 (2020)"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, B., Liu, Y., Miao, C.: Initialization matters: regularizing manifold-informed initialization for neural recommendation systems. In: KDD 2021, pp. 2263\u20132273 (2021)","DOI":"10.1145\/3447548.3467338"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW 2021, pp. 2069\u20132080 (2021)","DOI":"10.1145\/3442381.3449802"}],"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\/978-3-031-00126-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:11:45Z","timestamp":1650996705000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-00126-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031001253","9783031001260"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-00126-0_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 April 2022","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2022.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"543","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":"72","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":"76","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":"13% - 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":"6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was originally planned to take place in Hyberabad, India. 24 other papers are included in the volume.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}