{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:57:48Z","timestamp":1743145068395,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981809"},{"type":"electronic","value":"9789819981816"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8181-6_19","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"244-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["All You See Is the\u00a0Tip of\u00a0the\u00a0Iceberg: Distilling Latent Interactions Can Help You Find Treasures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6561-3471","authenticated-orcid":false,"given":"Zhuo","family":"Cai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-9817","authenticated-orcid":false,"given":"Guan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3550-5039","authenticated-orcid":false,"given":"Xiaobao","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8636-4996","authenticated-orcid":false,"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5953-9877","authenticated-orcid":false,"given":"Rui","family":"Bing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1133-9379","authenticated-orcid":false,"given":"Shoujin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: ICML, pp. 41\u201348 (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2022.02.070","volume":"488","author":"Z Cai","year":"2022","unstructured":"Cai, Z., Yuan, G., Qiao, S., Qu, S., Zhang, Y., Bing, R.: FG-CF: friends-aware graph collaborative filtering for poi recommendation. Neurocomputing 488, 107\u2013119 (2022)","journal-title":"Neurocomputing"},{"key":"19_CR3","first-page":"3183","volume":"26","author":"Z Cai","year":"2023","unstructured":"Cai, Z., Yuan, G., Zhuang, X., Wang, S., Qiao, S., Zhu, M.: Adaptive self-propagation graph convolutional network for recommendation. WWW 26, 3183\u20133206 (2023)","journal-title":"WWW"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Chae, D.K., Kang, J.S., Kim, S.W., Choi, J.: Rating augmentation with generative adversarial networks towards accurate collaborative filtering. In: WWW, pp. 2616\u20132622 (2019)","DOI":"10.1145\/3308558.3313413"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI, vol. 34, pp. 27\u201334 (2020)","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"19_CR6","first-page":"1094","volume":"33","author":"J Ding","year":"2020","unstructured":"Ding, J., Quan, Y., Yao, Q., Li, Y., Jin, D.: Simplify and robustify negative sampling for implicit collaborative filtering. Adv. Neural. Inf. Process. Syst. 33, 1094\u20131105 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Gunawardana, A., Shani, G., Yogev, S.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 547\u2013601. Springer, New York (2022). https:\/\/doi.org\/10.1007\/978-1-0716-2197-4_15","DOI":"10.1007\/978-1-0716-2197-4_15"},{"key":"19_CR8","unstructured":"Gupta, S., Wang, H., Lipton, Z., Wang, Y.: Correcting exposure bias for link recommendation. In: ICML, pp. 3953\u20133963. PMLR (2021)"},{"key":"19_CR9","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"19_CR10","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":"19_CR11","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":"19_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: UFNRec: utilizing false negative samples for sequential recommendation. In: ICDM, pp. 46\u201354. SIAM (2023)","DOI":"10.1137\/1.9781611977653.ch6"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Marlin, B.M., Zemel, R.S.: Collaborative prediction and ranking with non-random missing data. In: RecSys, pp. 5\u201312 (2009)","DOI":"10.1145\/1639714.1639717"},{"key":"19_CR15","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452\u2013461 (2009)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Song, W., Wang, S., Wang, Y., Wang, S.: Next-item recommendations in short sessions. In: RecSys, pp. 282\u2013291 (2021)","DOI":"10.1145\/3460231.3474238"},{"issue":"05","key":"19_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/MIS.2020.3026430","volume":"35","author":"S Wang","year":"2020","unstructured":"Wang, S., Pasi, G., Hu, L., Cao, L.: The era of intelligent recommendation: editorial on intelligent recommendation with advanced AI and learning. IEEE Intell. Syst. 35(05), 3\u20136 (2020)","journal-title":"IEEE Intell. Syst."},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Wang, S., Xu, X., Zhang, X., Wang, Y., Song, W.: Veracity-aware and event-driven personalized news recommendation for fake news mitigation. In: WWW, pp. 3673\u20133684 (2022)","DOI":"10.1145\/3485447.3512263"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, X., Wang, Y., Liu, H., Ricci, F.: Trustworthy recommender systems. arXiv preprint arXiv:2208.06265 (2022)","DOI":"10.1145\/3627826"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Wang, W., Feng, F., He, X., Nie, L., Chua, T.S.: Denoising implicit feedback for recommendation. In: WSDM, pp. 373\u2013381 (2021)","DOI":"10.1145\/3437963.3441800"},{"key":"19_CR21","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":"19_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: WSDM, pp. 153\u2013162 (2016)","DOI":"10.1145\/2835776.2835837"},{"key":"19_CR23","doi-asserted-by":"publisher","unstructured":"Yu, M., et al.: BPMCF: behavior preference mapping collaborative filtering for multi-behavior recommendation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing, ICONIP 2022. Communications in Computer and Information Science, vol. 1792, pp. 407\u2013418. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-1642-9_35","DOI":"10.1007\/978-981-99-1642-9_35"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8181-6_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:27:13Z","timestamp":1710329233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8181-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981809","9789819981816"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8181-6_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 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":"Changsha","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}