{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T18:59:14Z","timestamp":1759777154661,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031667602"},{"type":"electronic","value":"9783031667619"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-66761-9_13","type":"book-chapter","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T07:07:34Z","timestamp":1722928054000},"page":"142-155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting e-learning Course Purchases Using Deep Learning Based on Customer Retention"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1967-4219","authenticated-orcid":false,"given":"Pawe\u0142","family":"Golec","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3832-8154","authenticated-orcid":false,"given":"Marcin","family":"Hernes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomasz","family":"Sajewski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9119-2727","authenticated-orcid":false,"given":"Ewa","family":"Walaszczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7281-8253","authenticated-orcid":false,"given":"Artur","family":"Rot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8109-2175","authenticated-orcid":false,"given":"Marcin","family":"Fojcik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6946-6998","authenticated-orcid":false,"given":"Tomasz","family":"Turek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0006-703X","authenticated-orcid":false,"given":"Damian","family":"Dziembek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"issue":"9","key":"13_CR1","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1016\/j.jbusres.2012.12.008","volume":"66","author":"K Coussement","year":"2013","unstructured":"Coussement, K., De Bock, K.W.: Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. J. Bus. Res. 66(9), 1629\u20131636 (2013)","journal-title":"J. Bus. Res."},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Hassouna, M., Tarhini, A., Elyas, T., AbouTrab, M.S.: Customer churn in mobile markets a comparison of techniques. arXiv preprint arXiv:1607.07792 (2016)","DOI":"10.5539\/ibr.v8n6p224"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Bansal, M., Vyas, V.: Analysis and prediction of purchase intention of online customers with deep learning. In: Khanna, A., Polkowski, Z., Castillo, O. (eds.) Proceedings of Data Analytics and Management. Lecture Notes in Networks and Systems, vol 572. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-19-7615-5_16","DOI":"10.1007\/978-981-19-7615-5_16"},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"3836","DOI":"10.1016\/j.procs.2022.09.445","volume":"207","author":"D Kobiela","year":"2022","unstructured":"Kobiela, D., Krefta, D., Kr\u00f3l, W., Weichbroth, P.: ARIMA vs LSTM on NASDAQ stock exchange data. Procedia Comp. Sci. 207, 3836\u20133845 (2022)","journal-title":"Procedia Comp. Sci."},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Sundararaj, V., Rejeesh, M.R.: A detailed behavioral analysis on consumer and customer changing behavior concerning social networking sites. J. Retail. Cons. Ser. 58, 102190 (2021). ISSN 0969-6989, https:\/\/doi.org\/10.1016\/j.jretconser.2020.102190","DOI":"10.1016\/j.jretconser.2020.102190"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Guo, L., et al.: Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1984\u20131992 (2019)","DOI":"10.1145\/3292500.3330670"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Kao, C.-Y., Chueh, H.-E.: Deep learning based purchase forecasting for food producer-retailer team merchandising. Scientific Programming 2022, Article ID 2857850, 9 (2022). https:\/\/doi.org\/10.1155\/2022\/2857850","DOI":"10.1155\/2022\/2857850"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Nisha, Singh, A.S.: Customer behavior prediction using deep learning techniques for online purchasing. In: 2023 2nd International Conference for Innovation in Technology (INOCON), pp. 1\u20137. Bangalore, India (2023). https:\/\/doi.org\/10.1109\/INOCON57975.2023.10101102","DOI":"10.1109\/INOCON57975.2023.10101102"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Chaudhuri, N., Gupta, G., Vamsi, V., Bose, I.: On the platform but will they buy? Predicting customers\u2019 purchase behavior using deep learning. Decision Support Systems 149 (2021). https:\/\/doi.org\/10.1016\/j.dss.2021.113622","DOI":"10.1016\/j.dss.2021.113622"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Zhou, G., et al.: Deep Interest Network for Click-Through Rate Prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD\u2019 18), pp. 1059\u20131068. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3219819.3219823","DOI":"10.1145\/3219819.3219823"},{"key":"13_CR11","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based Recommendations with Recurrent Neural Networks. Computer Science, ICLR (2016). https:\/\/arxiv.org\/abs\/1511.06939"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Tang, J., Wang, K.: Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. WSDM \u201918: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining February 565\u2013573 (2018). https:\/\/doi.org\/10.1145\/3159652.3159656","DOI":"10.1145\/3159652.3159656"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Singhal, R., et al.: Fast online \u2019next best offers\u2019 using deep learning. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. CoDS-COMAD \u201919, pp. 217\u2013223. ACM, New York, NY, USA (2019)","DOI":"10.1145\/3297001.3297029"},{"key":"13_CR14","unstructured":"Vieira, A.: Predicting online user behaviour using deep learning algorithms. arXiv preprint arXiv:1511.06247 (2015)"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Huang, C., et al.: Online purchase prediction via multi-scale modeling of behavior dynamics. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2613\u20132622 (2019)","DOI":"10.1145\/3292500.3330790"},{"issue":"2","key":"13_CR16","first-page":"139","volume":"17","author":"N Spanoudakis","year":"2009","unstructured":"Spanoudakis, N., Moriaitis, P.: Engineering an agent-based system for product pricing automation. Eng. Intell. Syst. 17(2), 139 (2009)","journal-title":"Eng. Intell. Syst."},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Qureshi, S.A., Rehman, A.S., Qamar, A.M., Kamal, A., Rehman, A.: Telecommunication subscribers\u2019 churn prediction model using machine learning. In: Eighth International Conference on Digital Information Management (ICDIM 2013), pp. 131\u2013136. IEEE (2013)","DOI":"10.1109\/ICDIM.2013.6693977"},{"issue":"2","key":"13_CR18","first-page":"17","volume":"12","author":"G Premkumar","year":"2017","unstructured":"Premkumar, G., Rajan, J.: Customer retention in mobile telecom service market in india: opportunities and challenges. Ushus-Journal of Business Management 12(2), 17\u201329 (2017)","journal-title":"Ushus-Journal of Business Management"},{"issue":"10","key":"13_CR19","doi-asserted-by":"publisher","first-page":"6893","DOI":"10.1007\/s00521-018-3523-0","volume":"31","author":"CO Sakar","year":"2019","unstructured":"Sakar, C.O., Polat, S.O., Katircioglu, M., Kastro, Y.: Real-time prediction of online shoppers\u2019 purchasing intention using multi-layer perceptron and LSTM recurrent neural networks. Neural Comput. Appl. 31(10), 6893\u20136908 (2019)","journal-title":"Neural Comput. Appl."},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"DeLotell, P., Millam, L. yReinhardt, M.M.: The use of deep learning strategies in onlinebusiness courses to impact student retention. American J. Bus. Edu. 3(12), 49\u201356 (2010)","DOI":"10.19030\/ajbe.v3i12.964"},{"issue":"9","key":"13_CR21","first-page":"25","volume":"42","author":"UD Prasad","year":"2011","unstructured":"Prasad, U.D., Madhavi, S.: Prediction of churn behaviour of bank customers using data mining tools. Indian J. Market. 42(9), 25\u201330 (2011)","journal-title":"Indian J. Market."},{"issue":"2","key":"13_CR22","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1287\/mksc.2018.1129","volume":"38","author":"K Misra","year":"2019","unstructured":"Misra, K., Schwartz, E.M., Abernethy, J.: Dynamic online pricing with incomplete information using multiarmed bandit experiments. Mark. Sci. 38(2), 226\u2013252 (2019)","journal-title":"Mark. Sci."},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Sivasankari, S.S., et al.: Classification of diabetes using multi-layer perceptron. In: 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ICDCECE53908.2022.9793085"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Ali, O.M.A., Kareem, S.W., Mohammed, A.S.: Evaluation of electrocardiogram signals classification using CNN, SVM, and LSTM algorithm: A review. In: 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC), pp. 185\u2013191. IEEE (2022)","DOI":"10.1109\/IEC54822.2022.9807511"}],"container-title":["Lecture Notes in Networks and Systems","Emerging Challenges in Intelligent Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-66761-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T04:16:32Z","timestamp":1732594592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-66761-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031667602","9783031667619"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-66761-9_13","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"7 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The research was founded by a project: \u201cVouchers to support the innovation of Lower Silesian enterprises,\u201d Priority axis 1 Enterprises and innovations, Measure 1.2 Innovative Enterprises, Sub-measure 1.2.1 Innovative Enterprises \u2013 Horizontal Competition, Type 1.2.C.b Enterprise services - \u201cInnovation voucher. Project title: Innovative technology to improve the offer personalization process, increasing the customer retention level of CRP in Wroc\u0142aw.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Founding"}},{"value":"ECAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krak\u00f3w","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","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":"30 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecai2023.eu\/","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 and SMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","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":"69","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":"2.86","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":"2.15","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)"}},{"value":"Presented figures are relevant for the ECAI 2023 workshops","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)"}}]}}