{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:45:32Z","timestamp":1742942732089,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031085291"},{"type":"electronic","value":"9783031085307"}],"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\/978-3-031-08530-7_64","type":"book-chapter","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T12:13:00Z","timestamp":1661775180000},"page":"759-770","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimal User Categorization from a Hierarchical Clustering Tree for Recommendation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0649-8850","authenticated-orcid":false,"given":"Wei","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"64_CR1","unstructured":"Adomavicius, G., Bockstedt, J., Curley, S., Zhang, J.: Understanding effects of personalized vs. aggregate ratings on user preferences. In: Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, pp. 14\u201321 (2016)"},{"issue":"3","key":"64_CR2","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1007\/s10618-013-0311-4","volume":"27","author":"RJGB Campello","year":"2013","unstructured":"Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J.: A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Min. Knowl. Discov. 27(3), 344\u2013371 (2013). https:\/\/doi.org\/10.1007\/s10618-013-0311-4","journal-title":"Data Min. Knowl. Discov."},{"issue":"2","key":"64_CR3","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TAI.2021.3065894","volume":"2","author":"G Chao","year":"2021","unstructured":"Chao, G., Sun, S., Bi, J.: A survey on multiview clustering. IEEE Trans. Artif. Intell. 2(2), 146\u2013168 (2021)","journal-title":"IEEE Trans. Artif. Intell."},{"key":"64_CR4","doi-asserted-by":"crossref","unstructured":"Das, J., Majumder, S., Mali, K.: Clustering techniques to improve scalability and accuracy of recommender systems. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 29(4), 621\u2013651 (2021)","DOI":"10.1142\/S0218488521500276"},{"issue":"1","key":"64_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1644873.1644874","volume":"4","author":"Y Koren","year":"2010","unstructured":"Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discovery Data 4(1), 1\u201324 (2010)","journal-title":"ACM Trans. Knowl. Discovery Data"},{"key":"64_CR6","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ins.2020.05.021","volume":"534","author":"FSA Neto","year":"2020","unstructured":"Neto, F.S.A., Costa, A.F.D., Manzato, M.G., Campello, R.J.G.B.: Pre-processing approaches for collaborative filtering based on hierarchical clustering. Inf. Sci. 534, 172\u2013191 (2020)","journal-title":"Inf. Sci."},{"key":"64_CR7","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp.452\u2013461 (2009)"},{"key":"64_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-030-26773-5_14","volume-title":"Modeling Decisions for Artificial Intelligence","author":"W Song","year":"2019","unstructured":"Song, W., Li, X.: A Non-negative matrix factorization for recommender systems based on dynamic bias. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds.) MDAI 2019. LNCS (LNAI), vol. 11676, pp. 151\u2013163. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26773-5_14"},{"key":"64_CR9","doi-asserted-by":"crossref","unstructured":"Song, W., Liu, S.: Collaborative filtering based on clustering and simulated annealing. In: Proceedings of the 3rd International Conference on Big Data Engineering, pp.76\u201381 (2021)","DOI":"10.1145\/3468920.3468931"},{"key":"64_CR10","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/978-3-642-54927-4_62","volume-title":"Practical Applications of Intelligent Systems","author":"W Song","year":"2014","unstructured":"Song, W., Yang, K.: Personalized recommendation based on weighted sequence similarity. In: Wen, Z., Li, T. (eds.) Practical Applications of Intelligent Systems. AISC, vol. 279, pp. 657\u2013666. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-642-54927-4_62"},{"issue":"11","key":"64_CR11","doi-asserted-by":"publisher","first-page":"16599","DOI":"10.1007\/s11042-020-08884-9","volume":"80","author":"T Trinh","year":"2020","unstructured":"Trinh, T., Wu, D., Wang, R., Huang, J.Z.: An effective content-based event recommendation model. Multimedia Tools Appl. 80(11), 16599\u201316618 (2020). https:\/\/doi.org\/10.1007\/s11042-020-08884-9","journal-title":"Multimedia Tools Appl."},{"key":"64_CR12","doi-asserted-by":"crossref","unstructured":"Zheng, Y.: Utility-based multi-criteria recommender systems. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, pp. 2529\u20132531 (2019)","DOI":"10.1145\/3297280.3297641"}],"container-title":["Lecture Notes in Computer Science","Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08530-7_64","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:06:17Z","timestamp":1710259577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08530-7_64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031085291","9783031085307"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08530-7_64","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"30 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"19 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieaaie2022.wordpress.com\/","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":"127","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":"67","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":"14","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":"53% - 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":"5","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)"}}]}}