{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:04:40Z","timestamp":1764785080148,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031301070"},{"type":"electronic","value":"9783031301087"}],"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\/978-3-031-30108-7_28","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T04:03:04Z","timestamp":1681272184000},"page":"331-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CLCDR: Contrastive Learning for\u00a0Cross-Domain Recommendation to\u00a0Cold-Start Users"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1514-1116","authenticated-orcid":false,"given":"Yanyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9887-4301","authenticated-orcid":false,"given":"Yao","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7202-1922","authenticated-orcid":false,"given":"Wai Kin Victor","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Yue, X., et al.: Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13834\u201313844 (2021)","DOI":"10.1109\/CVPR46437.2021.01362"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., Huang, Z.: From zero-shot learning to cold-start recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4189\u20134196 (2019)","DOI":"10.1609\/aaai.v33i01.33014189"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Wu, T., Chio, E.K.I., Cheng, H.T., Du, Y.: Zero-shot heterogeneous transfer learning from recommender systems to cold-start search retrieval. In: Proceedings of the Conference on Information & Knowledge Management, pp. 2821\u20132828 (2010)","DOI":"10.1145\/3340531.3412752"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650\u2013658 (2008)","DOI":"10.1145\/1401890.1401969"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Mirbakhsh, N., Ling, C.X.: Improving top-n recommendation for cold-start users via cross-domain information. In: ACM Transactions on Knowledge Discovery from Data (TKDD), pp. 1\u201319 (2015)","DOI":"10.1145\/2724720"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Gao, C., et al.: Cross-domain recommendation without sharing user-relevant data. In: The World Wide Web Conference, pp. 491\u2013502 (2019)","DOI":"10.1145\/3308558.3313538"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Hu, G., Zhang, Y., Yang, Q.: Conet: collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 667\u2013676 (2018)","DOI":"10.1145\/3269206.3271684"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2464\u20132470 (2017)","DOI":"10.24963\/ijcai.2017\/343"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Fu, W., Peng, Z., Wang, S., Xu, Y., Li, J.: Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 94\u2013101 (2019)","DOI":"10.1609\/aaai.v33i01.330194"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Kang, S., Hwang, J., Lee, D., Yu, H.: Semi-supervised learning for cross-domain recommendation to cold-start users. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1563\u20131572 (2019)","DOI":"10.1145\/3357384.3357914"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Wang, B., Zhang, C., Zhang, H., Lyu, X., Tang, Z.: dual autoencoder network with swap reconstruction for cold-start recommendation. In: Proceedings of the Conference on Information and Knowledge Management, pp. 2249\u20132252 (2020)","DOI":"10.1145\/3340531.3412069"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Zhao, C., Li, C., Xiao, R., Deng, H., Sun, A.: CATN: cross-domain recommendation for cold-start users via aspect transfer network. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 229\u2013238 (2020)","DOI":"10.1145\/3397271.3401169"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Ni, J., Li, J., McAuley, J.: justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), pp. 188\u2013197 (2019)","DOI":"10.18653\/v1\/D19-1018"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Transfer-meta framework for cross-domain recommendation to cold-start users. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1813\u20131817 (2021)","DOI":"10.1145\/3404835.3463010"},{"key":"28_CR15","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 (2020)"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: collaborative filtering recommender systems. In: The Adaptive Web, pp. 291\u2013324 (2007)","DOI":"10.1007\/978-3-540-72079-9_9"},{"key":"28_CR19","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452\u2013461 (2009)"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Hsieh, C.K., Yang, L., Cui, Y., Lin, T.Y., Belongie, S., Estrin, D.: Collaborative Metric Learning. In: The World Wide Web Conference, pp. 193\u2013201 (2017)","DOI":"10.1145\/3038912.3052639"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"He, J., Liu, R., Fuzhen, Z., Fen, L., Qing, H.: A general cross-domain recommendation framework via bayesian neural network. In: IEEE International Conference on Data Mining (ICDM) (2018)","DOI":"10.1109\/ICDM.2018.00125"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: The World Wide Web Conference, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Zhu, F., Wang, Y., Chen, C., Liu, G., Zheng, X.: A graphical and attentional framework for dual-target cross-domain recommendation. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3001\u20133008 (2020)","DOI":"10.24963\/ijcai.2020\/415"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30108-7_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T03:20:41Z","timestamp":1729221641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30108-7_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301070","9783031301087"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30108-7_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"0","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":"44% - 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":"2.65","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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","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)"}}]}}