{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:52:17Z","timestamp":1787673137638,"version":"build-2736575974"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100020884","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["1240293"],"award-info":[{"award-number":["1240293"]}],"id":[{"id":"10.13039\/501100020884","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Electron Commer Res"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s10660-025-10021-3","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T02:52:45Z","timestamp":1753671165000},"page":"4011-4041","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of cart abandonment using imbalanced clickstream data in online shopping"],"prefix":"10.1007","volume":"26","author":[{"given":"Fabian","family":"Waldmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-0053","authenticated-orcid":false,"given":"Yamisleydi","family":"Salgueiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"10021_CR1","volume-title":"Fashion e-commerce worldwide\u2014Statistics & facts","author":"Statista","year":"2023","unstructured":"Statista. (2023). Fashion e-commerce worldwide\u2014Statistics & facts. Statista."},{"key":"10021_CR2","volume-title":"Cart abandonment rate statistics 2024","author":"Baymard-Institute","year":"2024","unstructured":"Baymard-Institute. (2024). Cart abandonment rate statistics 2024. Baymard-Institute."},{"key":"10021_CR3","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. (2019). Real-time prediction of online shoppers\u2019 purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 31, 6893\u20136908. https:\/\/doi.org\/10.1007\/s00521-018-3523-0","journal-title":"Neural Computing and Applications"},{"key":"10021_CR4","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6, 27. https:\/\/doi.org\/10.1186\/s40537-019-0192-5","journal-title":"Journal of Big Data"},{"issue":"3","key":"10021_CR5","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"2","author":"DL Wilson","year":"1972","unstructured":"Wilson, D. L. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics SMC, 2(3), 408\u2013421. https:\/\/doi.org\/10.1109\/TSMC.1972.4309137","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics SMC"},{"key":"10021_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10021_CR7","doi-asserted-by":"publisher","DOI":"10.4337\/9781800887374","volume-title":"Handbook on the politics and governance of big data and artificial intelligence","author":"A Zwitter","year":"2023","unstructured":"Zwitter, A. (2023). Handbook on the politics and governance of big data and artificial intelligence. Edward Elgar Publishing. https:\/\/doi.org\/10.4337\/9781800887374"},{"key":"10021_CR8","volume-title":"Artificial intelligence (AI): New developments and innovations applied to e-commerce","author":"D Pedreschi","year":"2020","unstructured":"Pedreschi, D., & Miliou, I. (2020). Artificial intelligence (AI): New developments and innovations applied to e-commerce. EPRS: European Parliamentary Research Service."},{"key":"10021_CR9","doi-asserted-by":"publisher","unstructured":"Nannini, L., Balayn, A., & Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the EU, US, and UK. In Proceedings of the 2023 ACM conference on fairness, accountability, and transparency. FAccT \u201923 (pp. 1198\u20131212). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3593013.3594074","DOI":"10.1145\/3593013.3594074"},{"key":"10021_CR10","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint https:\/\/arxiv.org\/abs\/arXiv.1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"10021_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Computation"},{"key":"10021_CR12","unstructured":"Elor, Y., & Averbuch-Elor, H. (2022). To SMOTE, or not to SMOTE? https:\/\/arxiv.org\/abs\/2201.08528"},{"key":"10021_CR13","unstructured":"Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv."},{"key":"10021_CR14","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s11747-009-0141-5","volume":"38","author":"M Kukar-Kinney","year":"2010","unstructured":"Kukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers\u2019 online shopping cart abandonment. Journal of the Academy of Marketing Science, 38, 240\u2013250. https:\/\/doi.org\/10.1007\/s11747-009-0141-5","journal-title":"Journal of the Academy of Marketing Science"},{"key":"10021_CR15","doi-asserted-by":"publisher","unstructured":"Guo, Q., & Agichtein, E. (2010). Ready to buy or just browsing? Detecting web searcher goals from interaction data. In Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201910 (pp. 130\u2013137). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/1835449.1835473","DOI":"10.1145\/1835449.1835473"},{"key":"10021_CR16","doi-asserted-by":"publisher","unstructured":"Hatt, T., & Feuerriegel, S. (2020). Early detection of user exits from clickstream data: A Markov modulated marked point process model. In Proceedings of the web conference 2020. WWW \u201920 (pp. 1671\u20131681). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3366423.3380238","DOI":"10.1145\/3366423.3380238"},{"key":"10021_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113342","volume":"150","author":"D Koehn","year":"2020","unstructured":"Koehn, D., Lessmann, S., & Schaal, M. (2020). Predicting online shopping behaviour from clickstream data using deep learning. Expert Systems with Applications, 150, Article 113342. https:\/\/doi.org\/10.1016\/j.eswa.2020.113342","journal-title":"Expert Systems with Applications"},{"key":"10021_CR18","doi-asserted-by":"publisher","first-page":"16983","DOI":"10.1038\/s41598-020-73622-y","volume":"10","author":"B Requena","year":"2020","unstructured":"Requena, B., Cassani, G., Tagliabue, J., Greco, C., & Lacasa, L. (2020). Shopper intent prediction from clickstream e-commerce data with minimal browsing information. Scientific Reports, 10, 16983. https:\/\/doi.org\/10.1038\/s41598-020-73622-y","journal-title":"Scientific Reports"},{"key":"10021_CR19","doi-asserted-by":"publisher","first-page":"36","DOI":"10.9790\/9622-1107023640","volume":"11","author":"P Reddy","year":"2021","unstructured":"Reddy, P., Sri, D., Reddy, C., & Shaik, S. (2021). Sentimental analysis using logistic regression. International Journal of Engineering Research and Applications, 11, 36\u201340. https:\/\/doi.org\/10.9790\/9622-1107023640","journal-title":"International Journal of Engineering Research and Applications"},{"key":"10021_CR20","unstructured":"Gazit, L., Ghaffari, M., & Saxena, A. (2024). Mastering NLP from foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python. Packt Publishing. https:\/\/books.google.de\/books?id=FzcAEQAAQBAJ"},{"key":"10021_CR21","unstructured":"Thomas, A. (2020). Natural language processing with spark NLP: Learning to understand text at scale. O\u2019Reilly Media. https:\/\/books.google.de\/books?id=5DDtDwAAQBAJ"},{"key":"10021_CR22","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1177\/1470785320972526","volume":"64","author":"TM Rausch","year":"2022","unstructured":"Rausch, T. M., Derra, N. D., & Wolf, L. (2022). Predicting online shopping cart abandonment with machine learning approaches. International Journal of Market Research, 64, 89\u2013112. https:\/\/doi.org\/10.1177\/1470785320972526","journal-title":"International Journal of Market Research"},{"key":"10021_CR23","unstructured":"Clark, A., & Taqa, A. (2024). Enhancing and exploring the use of transformer models in NLP tasks. International Journal of Transcontinental Discoveries, 11(1), 62\u201371. https:\/\/internationaljournals.org\/index.php\/ijtd\/article\/view\/109"},{"key":"10021_CR24","unstructured":"Keles, F. D., Wijewardena, P. M., & Hegde, C. (2022). On the computational complexity of self-attention. https:\/\/arxiv.org\/abs\/2209.04881"},{"key":"10021_CR25","doi-asserted-by":"publisher","unstructured":"Bayat, S., & Isik, G. (2023). Assessing the efficacy of LSTM, transformer, and RNN architectures in text summarization. In International conference on applied engineering and natural sciences (Vol. 1, pp. 813\u2013820). https:\/\/doi.org\/10.59287\/icaens.1099","DOI":"10.59287\/icaens.1099"},{"key":"10021_CR26","doi-asserted-by":"publisher","first-page":"3874","DOI":"10.1007\/978-981-19-6613-2_377","volume-title":"Advances in guidance, navigation and control","author":"D Zhou","year":"2023","unstructured":"Zhou, D., Zhang, Y., Li, Y., Li, K., Zhao, B., Wang, M., & Wang, N. (2023). Research on prediction method of UAV heat seeking navigation control based on GRU networks. In L. Yan, H. Duan, & Y. Deng (Eds.), Advances in guidance, navigation and control (pp. 3874\u20133881). Springer."},{"key":"10021_CR27","doi-asserted-by":"publisher","unstructured":"Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv 2014. arXiv preprint https:\/\/arxiv.org\/abs\/1412.3555, 1412. https:\/\/doi.org\/10.48550\/arXiv.1412.3555","DOI":"10.48550\/arXiv.1412.3555"},{"key":"10021_CR28","doi-asserted-by":"publisher","first-page":"161","DOI":"10.14419\/ijet.v7i4.6.20454","volume":"7","author":"TA Srinivas","year":"2018","unstructured":"Srinivas, T. A., Ramasubbareddy, S., Manivannan, S. S., & Govinda, K. (2018). Predicting user behaviour on e-commerce site using ANN. International Journal of Engineering and Technology (UAE), 7, 161\u2013164. https:\/\/doi.org\/10.14419\/ijet.v7i4.6.20454","journal-title":"International Journal of Engineering and Technology (UAE)"},{"key":"10021_CR29","doi-asserted-by":"publisher","first-page":"147","DOI":"10.54097\/p22ags78","volume":"15","author":"Z Liu","year":"2024","unstructured":"Liu, Z. (2024). Prediction model of e-commerce users\u2019 purchase behavior based on deep learning. Frontiers in Business, Economics and Management, 15, 147\u2013149. https:\/\/doi.org\/10.54097\/p22ags78","journal-title":"Frontiers in Business, Economics and Management"},{"key":"10021_CR30","doi-asserted-by":"publisher","unstructured":"Bogina, V., Kuflik, T., & Mokryn, O. (2016). Learning item temporal dynamics for predicting buying sessions. In Proceedings of the 21st international conference on intelligent user interfaces (pp. 251\u2013255). ACM. https:\/\/doi.org\/10.1145\/2856767.2856781","DOI":"10.1145\/2856767.2856781"},{"key":"10021_CR31","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s12525-020-00448-x","volume":"31","author":"R Esmeli","year":"2021","unstructured":"Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2021). Towards early purchase intention prediction in online session based retailing systems. Electronic Markets, 31, 697\u2013715. https:\/\/doi.org\/10.1007\/s12525-020-00448-x","journal-title":"Electronic Markets"},{"key":"10021_CR32","unstructured":"Bigon, L., Cassani, G., Greco, C., Lacasa, L., Pavoni, M., Polonioli, A., & Tagliabue, J. (2019). Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce. arXiv."},{"key":"10021_CR33","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.3390\/electronics11172737","volume":"11","author":"SA Alex","year":"2022","unstructured":"Alex, S. A., Jhanjhi, N., Humayun, M., Ibrahim, A. O., & Abulfaraj, A. W. (2022). Deep LSTM model for diabetes prediction with class balancing by smote. Electronics, 11, 2737. https:\/\/doi.org\/10.3390\/electronics11172737","journal-title":"Electronics"},{"key":"10021_CR34","doi-asserted-by":"publisher","unstructured":"Hooshyar, D., Azevedo, R., & Yang, Y. (2023). Augmenting deep neural networks with symbolic knowledge: Towards trustworthy and interpretable AI for education. Machine Learning and Knowledge Extraction, 6(1), 593\u2013618. https:\/\/doi.org\/10.3390\/make6010028","DOI":"10.3390\/make6010028"},{"key":"10021_CR35","doi-asserted-by":"publisher","DOI":"10.30837\/ITSSI.2023.24.161","author":"D Teslenko","year":"2023","unstructured":"Teslenko, D., Sorokina, A., Khovrat, A., Huliiev, N., & Kyriy, V. (2023). Comparison of dataset oversampling algorithms and their applicability to the categorization problem. Innovative Technologies and Scientific Solutions for Industries. https:\/\/doi.org\/10.30837\/ITSSI.2023.24.161","journal-title":"Innovative Technologies and Scientific Solutions for Industries"},{"key":"10021_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110895","volume":"148","author":"AX Wang","year":"2023","unstructured":"Wang, A. X., Chukova, S. S., & Nguyen, B. P. (2023). Synthetic minority oversampling using edited displacement-based k-nearest neighbors. Applied Soft Computing, 148, Article 110895. https:\/\/doi.org\/10.1016\/j.asoc.2023.110895","journal-title":"Applied Soft Computing"},{"key":"10021_CR37","doi-asserted-by":"publisher","unstructured":"Alejo, R., Sotoca, J. M., Valdovinos, R. M., & Toribio, P. (2010). Edited nearest neighbor rule for improving neural networks classifications (pp. 303\u2013310). https:\/\/doi.org\/10.1007\/978-3-642-13278-0_39","DOI":"10.1007\/978-3-642-13278-0_39"},{"key":"10021_CR38","doi-asserted-by":"publisher","unstructured":"Johnson, J. M., & Khoshgoftaar, T. M. (2021). In M. A. Wani, T. M. Khoshgoftaar, & V. Palade (Eds.), Thresholding strategies for deep learning with highly imbalanced big data (pp. 199\u2013227). Springer. https:\/\/doi.org\/10.1007\/978-981-15-6759-9_9","DOI":"10.1007\/978-981-15-6759-9_9"},{"key":"10021_CR39","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/978-3-662-44851-9_15","volume-title":"Machine learning and knowledge discovery in databases","author":"ZC Lipton","year":"2014","unstructured":"Lipton, Z. C., Elkan, C., & Naryanaswamy, B. (2014). Optimal thresholding of classifiers to maximize F1 measure. In T. Calders, F. Esposito, E. H\u00fcllermeier, & R. Meo (Eds.), Machine learning and knowledge discovery in databases (pp. 225\u2013239). Springer."},{"key":"10021_CR40","volume-title":"Understanding computers and cognition: A new foundation for design","author":"T Flores","year":"1986","unstructured":"Flores, T., Flores, T. T., & Winograd, T. (1986). Understanding computers and cognition: A new foundation for design. Intellect Books."},{"key":"10021_CR41","doi-asserted-by":"publisher","first-page":"31","DOI":"10.33847\/2712-8148.4.1_4","volume":"4","author":"N Thalpage","year":"2023","unstructured":"Thalpage, N. (2023). Unlocking the black box: Explainable artificial intelligence (XAI) for trust and transparency in AI systems. Journal of Digital Art & Humanities, 4, 31\u201336. https:\/\/doi.org\/10.33847\/2712-8148.4.1_4","journal-title":"Journal of Digital Art & Humanities"},{"key":"10021_CR42","doi-asserted-by":"publisher","first-page":"100700","DOI":"10.1109\/ACCESS.2022.3207765","volume":"10","author":"A Theissler","year":"2022","unstructured":"Theissler, A., Spinnato, F., Schlegel, U., & Guidotti, R. (2022). Explainable AI for time series classification: A review, taxonomy and research directions. IEEE Access, 10, 100700\u2013100724. https:\/\/doi.org\/10.1109\/ACCESS.2022.3207765","journal-title":"IEEE Access"},{"key":"10021_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100230","volume":"7","author":"A Saranya","year":"2023","unstructured":"Saranya, A., & Subhashini, R. (2023). A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends. Decision Analytics Journal, 7, Article 100230. https:\/\/doi.org\/10.1016\/j.dajour.2023.100230","journal-title":"Decision Analytics Journal"},{"key":"10021_CR44","unstructured":"Molnar, C. (2022). Interpretable machine learning: A guide for making black box models interpretable (2nd ed.). OCLC, 1105728953(10). https:\/\/christophm.github.io\/interpretable-ml-book\/"},{"key":"10021_CR45","doi-asserted-by":"publisher","unstructured":"Roshan, K., & Zafar, A. (2023). Using kernel SHAP XAI method to optimize the network anomaly detection model. In IEEE 9th international conference on computing for sustainable global development (INDIACom) (pp. 74\u201380). https:\/\/doi.org\/10.23919\/INDIACom54597.2022.9763241","DOI":"10.23919\/INDIACom54597.2022.9763241"},{"key":"10021_CR46","unstructured":"Villani, M., Lockhart, J., & Magazzeni, D. (2022). Feature importance for time series data: Improving KernelSHAP. arXiv. https:\/\/arxiv.org\/abs\/2210.02176"},{"key":"10021_CR47","doi-asserted-by":"publisher","unstructured":"Chandramohan, T. N., & Ravindran, B. (2018). A neural attention based approach for clickstream mining. In Proceedings of the ACM India joint international conference on data science and management of data. CODS-COMAD \u201918 (pp. 118\u2013127). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3152494.3152505","DOI":"10.1145\/3152494.3152505"},{"key":"10021_CR48","unstructured":"Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H., & Zhao, B. Y. (2013). You are how you click: Clickstream analysis for Sybil detection. In Proceedings of the 22nd USENIX conference on security. SEC\u201913 (pp. 241\u2013256). USENIX Association."},{"key":"10021_CR49","unstructured":"Sheil, H., Rana, O., & Reilly, R. (2020). Understanding ecommerce clickstreams: A tale of two states. In KDD deep learning day. ACM (2018)."},{"key":"10021_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-023-05259-9","author":"G Mena","year":"2023","unstructured":"Mena, G., Coussement, K., Bock, K., Caigny, A. D., & Lessmann, S. (2023). Exploiting time-varying RFM measures for customer churn prediction with deep neural networks. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-023-05259-9","journal-title":"Annals of Operations Research"},{"key":"10021_CR51","doi-asserted-by":"publisher","unstructured":"Tagliabue, J., Greco, C., Roy, J.-F., Yu, B., Chia, P. J., Bianchi, F., & Cassani, G. (2021). SIGIR 2021 e-commerce workshop data challenge. arXiv, Cornell University. https:\/\/doi.org\/10.48550\/arXiv.2104.09423","DOI":"10.48550\/arXiv.2104.09423"},{"key":"10021_CR52","unstructured":"Toth, A. R., Tan, L. H. S., Fabbrizio, G. D., & Datta, A. (2017). Predicting shopping behavior with mixture of RNNs. In eCOM@SIGIR."},{"key":"10021_CR53","unstructured":"Vajjala, S., Majumder, B., Gupta, A., & Surana, H. (2020). Practical natural language processing: A comprehensive guide to building real-world NLP systems. O\u2019Reilly Media. https:\/\/books.google.de\/books?id=hvrrDwAAQBAJ"},{"key":"10021_CR54","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, S., Banerjee, J. S., De, D., & Mahmud, M. (2023). Intelligent human centered computing: Proceedings of HUMAN 2023. Springer tracts in human-centered computing. Springer. https:\/\/books.google.de\/books?id=M2LFEAAAQBAJ","DOI":"10.1007\/978-981-99-3478-2"},{"key":"10021_CR55","doi-asserted-by":"publisher","unstructured":"Cahuantzi, R., Chen, X., & G\u00fcttel, S. (2021). A comparison of LSTM and GRU networks for learning symbolic sequences. arXiv. https:\/\/doi.org\/10.1007\/978-3-031-37963-5_53","DOI":"10.1007\/978-3-031-37963-5_53"},{"key":"10021_CR56","unstructured":"Cholle t, F. (2015). Keras. https:\/\/keras.io"},{"key":"10021_CR57","doi-asserted-by":"publisher","first-page":"849","DOI":"10.3390\/e22080849","volume":"22","author":"W Wegier","year":"2020","unstructured":"Wegier, W., & Ksieniewicz, P. (2020). Application of imbalanced data classification quality metrics as weighting methods of the ensemble data stream classification algorithms. Entropy, 22, 849. https:\/\/doi.org\/10.3390\/e22080849","journal-title":"Entropy"},{"key":"10021_CR58","doi-asserted-by":"publisher","unstructured":"Arslan, Y., Lebichot, B., Allix, K., Veiber, L., Lefebvre, C., Boytsov, A., Goujon, A., Bissyande, T., & Klein, J. (2022). On the suitability of SHAP explanations for refining classifications. In Proceedings of the 14th international conference on agents and artificial intelligence (ICAART 2022) (pp. 395\u2013402). SCITEPRESS - Science and Technology Publications. https:\/\/doi.org\/10.5220\/0010827700003116","DOI":"10.5220\/0010827700003116"},{"key":"10021_CR59","doi-asserted-by":"crossref","unstructured":"Doan, T., Veira, N., Ray, S., & Keng, B. (2019). Generating realistic sequences of customer-level transactions for retail datasets. arXiv.","DOI":"10.1109\/ICDMW.2018.00122"},{"key":"10021_CR60","doi-asserted-by":"publisher","unstructured":"Zhao, P., Luo, C., Qiao, B., Wang, L., Rajmohan, S., Lin, Q., & Zhang, D. (2022). T-SMOTE: Temporal-oriented synthetic minority oversampling technique for imbalanced time series classification. In Raedt, L. D. (Ed.), Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI-22 (pp. 2406\u20132412). International Joint Conferences on Artificial Intelligence Organization. https:\/\/doi.org\/10.24963\/ijcai.2022\/334","DOI":"10.24963\/ijcai.2022\/334"}],"container-title":["Electronic Commerce Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10660-025-10021-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10660-025-10021-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10660-025-10021-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T06:30:22Z","timestamp":1786343422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10660-025-10021-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,28]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["10021"],"URL":"https:\/\/doi.org\/10.1007\/s10660-025-10021-3","relation":{},"ISSN":["1389-5753","1572-9362"],"issn-type":[{"value":"1389-5753","type":"print"},{"value":"1572-9362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,28]]},"assertion":[{"value":"4 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}