{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:54:15Z","timestamp":1773248055185,"version":"3.50.1"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031306938","type":"print"},{"value":"9783031306945","type":"electronic"}],"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\/978-3-031-30694-5_8","type":"book-chapter","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T06:03:12Z","timestamp":1681884192000},"page":"91-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Digital Content Profiling Based on\u00a0User Engagement Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5223-240X","authenticated-orcid":false,"given":"Pawel","family":"Misiorek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7742-6590","authenticated-orcid":false,"given":"Michal","family":"Ciesielczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bartosz","family":"Rzycki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Carlton, J., Brown, A., Jay, C., Keane, J.: Using Interaction Data to Predict Engagement with Interactive Media, pp. 1258\u20131266. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3474085.3475631","DOI":"10.1145\/3474085.3475631"},{"issue":"10","key":"8_CR2","doi-asserted-by":"publisher","first-page":"3394","DOI":"10.1109\/TKDE.2020.2969419","volume":"33","author":"H Davoudi","year":"2021","unstructured":"Davoudi, H., Rashidi, Z., An, A., Zihayat, M., Edall, G.: Paywall policy learning in digital news media. IEEE Trans. Knowl. Data Eng. 33(10), 3394\u20133409 (2021). https:\/\/doi.org\/10.1109\/TKDE.2020.2969419","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Davoudi, H., Zihayat, M., An, A.: Time-aware subscription prediction model for user acquisition in digital news media. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 135\u2013143. SIAM (2017). https:\/\/doi.org\/10.1137\/1.9781611974973.16","DOI":"10.1137\/1.9781611974973.16"},{"key":"8_CR4","unstructured":"Dorogush, A.V., Gulin, A., Gusev, G., Kazeev, N., Prokhorenkova, L.O., Vorobev, A.: Fighting biases with dynamic boosting. CoRR abs\/1706.09516 (2017). http:\/\/arxiv.org\/abs\/1706.09516"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Fei, Y., Lv, C., Feng, Y., Zhao, D.: Real-time filtering on interest profiles in twitter stream. In: Proceedings of the 16th ACM\/IEEE-CS on Joint Conference on Digital Libraries, pp. 263\u2013264. JCDL 2016, ACM, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2910896.2925462","DOI":"10.1145\/2910896.2925462"},{"key":"8_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511973000","volume-title":"Machine Learning: The Art and Science of Algorithms That Make Sense of Data","author":"P Flach","year":"2012","unstructured":"Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, New York, NY, USA (2012)"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Grinberg, N.: Identifying modes of user engagement with online news and their relationship to information gain in text. In: Proceedings of the 2018 World Wide Web Conference, pp. 1745\u20131754. WWW \u201918 (2018). https:\/\/doi.org\/10.1145\/3178876.3186180","DOI":"10.1145\/3178876.3186180"},{"key":"8_CR8","unstructured":"Li, H., Vu, Q.H., Pham, T.L., Nguyen, T.T., Chen, S., Lee, S.: An ensemble approach to streaming service churn prediction. In: WSDM Cup 2018 Workshop, The 11th ACM International Conference on Web Search and Data Mining, Los Angeles, California, USA, pp. 1\u20138 (2018). https:\/\/wsdm-cup-2018.kkbox.events\/"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Liang, S.: Collaborative, dynamic and diversified user profiling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 4269\u20134276 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33014269","DOI":"10.1609\/aaai.v33i01.33014269"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Madnani, N., Loukina, A., Cahill, A.: A large scale quantitative exploration of modeling strategies for content scoring. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 457\u2013467 (2017). https:\/\/doi.org\/10.18653\/v1\/W17-5052","DOI":"10.18653\/v1\/W17-5052"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Misiorek, P., Warmuz, J., Kaczmarek, D., Ciesielczyk, M.: Modeling user engagement profiles for detection of digital subscription propensity. In: Themistocleous, M., Papadaki, M. (eds.) Information Systems. EMCIS 2021. LNBIP, vol. 437, pp. 55\u201368. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-95947-0_5","DOI":"10.1007\/978-3-030-95947-0_5"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Riordan, B., Flor, M., Pugh, R.: How to account for mispellings: quantifying the benefit of character representations in neural content scoring models. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 116\u2013126 (2019). https:\/\/doi.org\/10.18653\/v1\/W19-4411","DOI":"10.18653\/v1\/W19-4411"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"Tang, X., Xu, Y., Geva, S.: Integrating time forgetting mechanisms into topic-based user interest profiling. In: 2013 IEEE\/WIC\/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 1\u20134 (2013). https:\/\/doi.org\/10.1109\/WI-IAT.2013.132","DOI":"10.1109\/WI-IAT.2013.132"},{"key":"8_CR14","unstructured":"Yandex: Catboost - open-source gradient boosting library. https:\/\/catboost.ai\/. Accessed October 2022"}],"container-title":["Lecture Notes in Business Information Processing","Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30694-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T06:05:27Z","timestamp":1681884327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30694-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306938","9783031306945"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30694-5_8","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EMCIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European, Mediterranean, and Middle Eastern Conference on Information Systems","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":"21 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"emcis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/emcis.eu\/","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":"Easyacademic.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"136","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":"47","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":"35% - 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","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","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)"}}]}}