{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:52:44Z","timestamp":1740099164628,"version":"3.37.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030014360"},{"type":"electronic","value":"9783030014377"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01437-7_15","type":"book-chapter","created":{"date-parts":[[2018,9,24]],"date-time":"2018-09-24T06:03:53Z","timestamp":1537769033000},"page":"181-193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Similarity Measures and Models for Movie Series Recommender System"],"prefix":"10.1007","author":[{"given":"Bliznuk","family":"Danil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yagunova","family":"Elena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pronoza","family":"Ekaterina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"15_CR1","unstructured":"Gurbanov, T.: Non-personalized recommendations: method of associations. https:\/\/habrahabr.ru\/post\/257903\/ . Accessed 1 May 2018"},{"key":"15_CR2","unstructured":"Roizner, M.: How recommender systems work. https:\/\/habrahabr.ru\/company\/dca\/blog\/280700\/ . Accessed 1 May 2018"},{"key":"15_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3","volume-title":"Recommender Systems Handbook","year":"2011","unstructured":"Ricci, F., Rokach, L., Shapira, B.: Introduction ton to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B., (eds.) Recommender Systems Handbook, pp. 1\u201329 (2011). ISBN 978-0-387-85819-7, https:\/\/doi.org\/10.1007\/978-0-387-85820-3"},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-0-387-85820-3_3","volume-title":"Recommender Systems Handbook","author":"Pasquale Lops","year":"2010","unstructured":"Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook. ISBN 978-0-387-85819-7, pp. 73\u2013100 (2010). https:\/\/doi.org\/10.1007\/978-0-387-85820-3_3"},{"key":"15_CR5","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS\u201913 Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, pp. 3111\u20133119 (2013)"},{"key":"15_CR6","first-page":"88","volume":"2","author":"Y Tambovcev","year":"2008","unstructured":"Tambovcev, Y., Tambovceva, A., Tambovceva, L.: Typology of linguistic units distribution in text as a factor in author profiling task. Vestnik Omskogo universiteta 2, 88\u201396 (2008)","journal-title":"Vestnik Omskogo universiteta"},{"key":"15_CR7","unstructured":"Pospelova, A., Yagunova, E.: The use of stylistic and genre characteristics to describe text collection style. Novie informacionnie tehnologii v avtomatizirovannih systemah, pp. 347\u2013357 (2014)"},{"issue":"3","key":"15_CR8","first-page":"83","volume":"1","author":"E Yagunova","year":"2014","unstructured":"Yagunova, E., Pivovarova, L.: Experimental and computational study of N.V.Gogol\u2019 narrative stories. Struct. Funct. Stud. Russ. Linguist. 1(3), 83\u2013104 (2014)","journal-title":"Struct. Funct. Stud. Russ. Linguist."},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-319-43808-5_13","volume-title":"Human Language Technology. Challenges for Computer Science and Linguistics","author":"Adam Wojciechowski","year":"2016","unstructured":"Wojciechowski, A., Goeznynski, K.: A method for measuring similarity of books: a step towards an objective recommender system for readers. In: Human Language Technology. Challenges for Computer Science and Linguistics, pp. 161\u2013174 (2016). https:\/\/doi.org\/10.1007\/978-3-319-43808-5_13"},{"key":"15_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-27060-9","volume-title":"Advances in Artificial Intelligence and Soft Computing","year":"2015","unstructured":"Pronoza, E., Yagunova, E.: Low-level features for paraphrase identification. Adv. Artif. Intell. Soft Comput. 59\u201371 (2015). https:\/\/doi.org\/10.1007\/978-3-319-27060-9"},{"key":"15_CR11","unstructured":"Movie2Vec: Clustering movies by plot. https:\/\/movie2vec.wordpress.com\/2016\/03\/22\/clustering-movies-by-plot\/ . Accessed 1 May 2018"},{"key":"15_CR12","unstructured":"Paramonov, S.: How to write a simple recommender system. https:\/\/habrahabr.ru\/post\/230155\/ . Accessed 1 May 2018"},{"key":"15_CR13","unstructured":"Recommender systems: introduction to the cold start problem. https:\/\/habrahabr.ru\/company\/surfingbird\/blog\/168733\/ . Accessed 1 May 2018"},{"key":"15_CR14","unstructured":"Bordashshenko, A., Potemkin, A., Sazanova, E., Shekshuev, S.: Algorithm for the search of similar media reports. Int. J. \u201cNaukovedenie\u201d 7 (2015). ISSN 2223-5167"},{"issue":"4","key":"15_CR15","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1353\/lan.2015.0068","volume":"91","author":"M Mysl\u00edn","year":"2015","unstructured":"Mysl\u00edn, M., Levy, R.: Codeswitching and predictability of meaning in discourse. Language 91(4), 871\u2013905 (2015). https:\/\/doi.org\/10.1353\/lan.2015.0068","journal-title":"Language"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Song, Y., Roth, D.: Unsupervised sparse vector densification for short text similarity. In: NAACL HLT 2015\u20142015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, pp. 1275\u20131280 (2015)","DOI":"10.3115\/v1\/N15-1138"},{"key":"15_CR17","volume-title":"Information Theory, Inference, and Learning Algorithms","author":"D MacKay","year":"2003","unstructured":"MacKay, D.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)"},{"key":"15_CR18","unstructured":"Manning, C., Raghavan, P., Sch\u00fctze, H.: Introduction to Information Retrieval. Williams (2014). ISBN 978-5-8459-1623-5"},{"key":"15_CR19","unstructured":"Scripted Originals Hit Record 455 in 2016. FX Study Finds. https:\/\/www.hollywoodreporter.com\/live-feed\/scripted-originals-hit-record-455-2016-fx-study-finds-958337 . Accessed 1 May 2018"},{"key":"15_CR20","unstructured":"Era of Peak TV Continues With 487 Scripted Shows in 2017. https:\/\/www.wsj.com\/articles\/era-of-peak-tv-continues-with-487-scripted-shows-in-2017-1515182593 . Accessed 1 May 2018"},{"key":"15_CR21","unstructured":"Best movie series. https:\/\/www.kinopoisk.ru\/top\/lists\/45\/ . Accessed 1 May 2018"},{"key":"15_CR22","unstructured":"The most popular movie series in Kinopoisk. https:\/\/www.kinopoisk.ru\/top\/lists\/257\/ . Accessed 1 May 2018"},{"key":"15_CR23","unstructured":"Gensim. https:\/\/radimrehurek.com\/gensim\/ . Accessed 1 May 2018"},{"key":"15_CR24","unstructured":"RusVect\u014dr\u0113s: Russian semantic models. http:\/\/rusvectores.org\/ru\/ . Accessed 1 May 2018"},{"key":"15_CR25","unstructured":"Russian Distributional Thesaurus. https:\/\/nlpub.ru\/Russian_Distributional_Thesaurus . Accessed 1 May 2018"},{"key":"15_CR26","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space"},{"key":"15_CR27","unstructured":"word2vec. https:\/\/code.google.com\/archive\/p\/word2vec\/ . Accessed 1 May 2018"},{"key":"15_CR28","unstructured":"Hierarchical clustering. https:\/\/docs.scipy.org\/doc\/scipy\/reference\/cluster.hierarchy.html . Accessed 1 May 2018"},{"key":"15_CR29","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226\u2013231 (1996)"},{"issue":"5814","key":"15_CR30","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"B. J. Frey","year":"2007","unstructured":"Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315 (2007). https:\/\/doi.org\/10.1126\/science.1136800","journal-title":"Science"},{"key":"15_CR31","doi-asserted-by":"publisher","first-page":"236","DOI":"10.2307\/2282967","volume":"58","author":"JH Ward Jr","year":"1963","unstructured":"Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236\u2013244 (1963). https:\/\/doi.org\/10.2307\/2282967","journal-title":"J. Am. Stat. Assoc."},{"key":"15_CR32","unstructured":"AffinityPropagation. http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.AffinityPropagation.html . Accessed 1 May 2018"}],"container-title":["Lecture Notes in Computer Science","Internet Science"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01437-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T14:32:07Z","timestamp":1571927527000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01437-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014360","9783030014377"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01437-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"INSCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Internet Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"insci2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/insci2018.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"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"73","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"32% - 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"}},{"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"}},{"value":"3.25","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}