{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:09:01Z","timestamp":1765544941478,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,4]]},"DOI":"10.1145\/3632410.3632452","type":"proceedings-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T18:15:16Z","timestamp":1704305716000},"page":"350-358","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1763-7249","authenticated-orcid":false,"given":"Arnab","family":"Chakraborty","sequence":"first","affiliation":[{"name":"A2D-AI, Intuit, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3252-6181","authenticated-orcid":false,"given":"Vikas","family":"Raturi","sequence":"additional","affiliation":[{"name":"A2D-AI, Intuit, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0834-4903","authenticated-orcid":false,"given":"Shrutendra","family":"Harsola","sequence":"additional","affiliation":[{"name":"A2D-AI, Intuit, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics","author":"Adeniyi David\u00a0Adedayo","year":"2016","unstructured":"David\u00a0Adedayo Adeniyi, Zhaoqiang Wei, and Yang Yongquan. 2016. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics (2016)."},{"key":"e_1_3_2_1_2_1","volume-title":"Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data","author":"Ahmad Abdelrahim\u00a0Kasem","year":"2019","unstructured":"Abdelrahim\u00a0Kasem Ahmad, Assef Jafar, and Kadan Aljoumaa. 2019. Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data (2019)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Shelby\u00a0D Bernhard Carson\u00a0K Leung Vanessa\u00a0J Reimer and Joshua Westlake. 2016. Clickstream prediction using sequential stream mining techniques with Markov chains. In IDEAS.","DOI":"10.1145\/2938503.2938535"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIG.2017.8080412"},{"key":"e_1_3_2_1_5_1","unstructured":"Veronika Bogina and Tsvi Kuflik. 2017. Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks.. In RecTemp@ RecSys. 57\u201359."},{"key":"e_1_3_2_1_6_1","volume-title":"Handling class imbalance in customer churn prediction. Expert Systems with Applications","author":"Burez Jonathan","year":"2009","unstructured":"Jonathan Burez and Dirk Van\u00a0den Poel. 2009. Handling class imbalance in customer churn prediction. Expert Systems with Applications (2009)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481915"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_1_9_1","volume-title":"Designing a Real-Time Data-Driven Customer Churn Risk Indicator for Subscription Commerce. IJIEEB","author":"Deligiannis Alexandros","year":"2020","unstructured":"Alexandros Deligiannis and Charalampos Argyriou. 2020. Designing a Real-Time Data-Driven Customer Churn Risk Indicator for Subscription Commerce. IJIEEB (2020)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Joana Dias Pedro Godinho and Pedro Torres. 2020. Machine Learning for Customer Churn Prediction in Retail Banking. In ICCSA.","DOI":"10.1007\/978-3-030-58808-3_42"},{"key":"e_1_3_2_1_11_1","volume-title":"Learning user real-time intent for optimal dynamic web page transformation. Information Systems Research","author":"Ding Amy\u00a0Wenxuan","year":"2015","unstructured":"Amy\u00a0Wenxuan Ding, Shibo Li, and Patrali Chatterjee. 2015. Learning user real-time intent for optimal dynamic web page transformation. Information Systems Research (2015)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/SoCPaR.2009.97"},{"key":"e_1_3_2_1_13_1","unstructured":"Yulong Gu Zhuoye Ding Shuaiqiang Wang and Dawei Yin. 2020. Hierarchical user profiling for e-commerce recommender systems. In WSDM."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"\u015eule G\u00fcnd\u00fcz and M\u00a0Tamer \u00d6zsu. 2003. A web page prediction model based on click-stream tree representation of user behavior. In KDD. 535\u2013540.","DOI":"10.1145\/956750.956815"},{"key":"e_1_3_2_1_15_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380238"},{"key":"e_1_3_2_1_17_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Yiqing Huang Fangzhou Zhu Mingxuan Yuan Ke Deng Yanhua Li Bing Ni Wenyuan Dai Qiang Yang and Jia Zeng. 2015. Telco churn prediction with big data. In SIGMOD.","DOI":"10.1145\/2723372.2742794"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Hemant Ishwaran Udaya\u00a0B Kogalur Eugene\u00a0H Blackstone and Michael\u00a0S Lauer. 2008. Random survival forests. (2008).","DOI":"10.1214\/08-AOAS169"},{"key":"e_1_3_2_1_20_1","volume-title":"Clickgraph: Web page embedding using clickstream data for multitask learning. In WWW.","author":"Jenkins Porter","year":"2019","unstructured":"Porter Jenkins. 2019. Clickgraph: Web page embedding using clickstream data for multitask learning. In WWW."},{"key":"e_1_3_2_1_21_1","volume-title":"DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology 18, 1","author":"Katzman L","year":"2018","unstructured":"Jared\u00a0L Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2018. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology 18, 1 (2018), 1\u201312."},{"key":"e_1_3_2_1_22_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. NeurIPS","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. NeurIPS (2017)."},{"volume-title":"Resampling methods for dependent data","author":"Lahiri SN","key":"e_1_3_2_1_23_1","unstructured":"SN Lahiri. 2003. Resampling methods for dependent data. Springer Science & Business Media."},{"key":"e_1_3_2_1_24_1","volume-title":"Theoretical comparisons of block bootstrap methods. Annals of Statistics","author":"Lahiri N","year":"1999","unstructured":"Soumendra\u00a0N Lahiri. 1999. Theoretical comparisons of block bootstrap methods. Annals of Statistics (1999)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467277"},{"key":"e_1_3_2_1_26_1","unstructured":"Jing Li Pengjie Ren Zhumin Chen Zhaochun Ren Tao Lian and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"volume-title":"Predicting customer churn in the telecommunications industry\u2014-An application of survival analysis modeling using SAS","author":"Junxiang Lu.","key":"e_1_3_2_1_28_1","unstructured":"Junxiang Lu. 2002. Predicting customer churn in the telecommunications industry\u2014-An application of survival analysis modeling using SAS. In SAS User Group International (SUGI27) Online Proceedings, Vol.\u00a0114."},{"volume-title":"Weibull Time To Event Recurrent Neural Network. Master\u2019s thesis","author":"Martinsson Egil","key":"e_1_3_2_1_29_1","unstructured":"Egil Martinsson. 2016. WTTE-RNN : Weibull Time To Event Recurrent Neural Network. Master\u2019s thesis. Chalmers University Of Technology."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71970-2_11"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Susan\u00a0A Murphy and Aad\u00a0W Van\u00a0der Vaart. 2000. On profile likelihood. J. Amer. Statist. Assoc. (2000).","DOI":"10.2307\/2669386"},{"key":"e_1_3_2_1_32_1","volume-title":"A latent-class model for estimating product-choice probabilities from clickstream data. Information Sciences","author":"Nishimura Naoki","year":"2018","unstructured":"Naoki Nishimura, Noriyoshi Sukegawa, Yuichi Takano, and Jiro Iwanaga. 2018. A latent-class model for estimating product-choice probabilities from clickstream data. Information Sciences (2018)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512027"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Massimo Quadrana Alexandros Karatzoglou Bal\u00e1zs Hidasi and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In RecSys.","DOI":"10.1145\/3109859.3109896"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1287\/msom.2015.0547"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_37_1","volume-title":"Profile likelihood and conditionally parametric models. The Annals of statistics","author":"Severini A","year":"1992","unstructured":"Thomas\u00a0A Severini and Wing\u00a0Hung Wong. 1992. Profile likelihood and conditionally parametric models. The Annals of statistics (1992)."},{"key":"e_1_3_2_1_38_1","volume-title":"Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems 41","author":"\u0160trumbelj Erik","year":"2014","unstructured":"Erik \u0160trumbelj and Igor Kononenko. 2014. Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems 41 (2014), 647\u2013665."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM.","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_40_1","volume-title":"Multiclass classification evaluation with ROC Curves and ROC AUC. https:\/\/towardsdatascience.com\/multiclass-classification-evaluation-with-roc-curves-and-roc-auc-294fd4617e3a Accessed on","author":"Trevisan Vin\u00edcius","year":"2023","unstructured":"Vin\u00edcius Trevisan. 2022. Multiclass classification evaluation with ROC Curves and ROC AUC. https:\/\/towardsdatascience.com\/multiclass-classification-evaluation-with-roc-curves-and-roc-auc-294fd4617e3a Accessed on March 20, 2023."},{"key":"e_1_3_2_1_41_1","volume-title":"A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory","author":"Vafeiadis Thanasis","year":"2015","unstructured":"Thanasis Vafeiadis, Konstantinos\u00a0I Diamantaras, George Sarigiannidis, and K\u00a0Ch Chatzisavvas. 2015. A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory (2015)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.intmar.2011.07.001"},{"key":"e_1_3_2_1_43_1","volume-title":"Predicting online user behaviour using deep learning algorithms. arXiv preprint arXiv:1511.06247","author":"Vieira Armando","year":"2015","unstructured":"Armando Vieira. 2015. Predicting online user behaviour using deep learning algorithms. arXiv preprint arXiv:1511.06247 (2015)."},{"key":"e_1_3_2_1_44_1","volume-title":"Lads: Deep Survival Analysis for Churn Prediction Analysis in the Contract User Domain. In 2022 14th International Conference on Machine Learning and Computing (ICMLC). 237\u2013244","author":"Xu Feng","year":"2022","unstructured":"Feng Xu, Hao Zhang, Juan Zheng, Ting Ting\u00a0Zhao, Xi Dong\u00a0Wang, and Zhi Yong\u00a0Zeng. 2022. Lads: Deep Survival Analysis for Churn Prediction Analysis in the Contract User Domain. In 2022 14th International Conference on Machine Learning and Computing (ICMLC). 237\u2013244."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Carl Yang Xiaolin Shi Luo Jie and Jiawei Han. 2018. I know you\u2019ll be back: Interpretable new user clustering and churn prediction on a mobile social application. In KDD.","DOI":"10.1145\/3219819.3219821"},{"key":"e_1_3_2_1_46_1","volume-title":"Conversion prediction from clickstream: Modeling market prediction and customer predictability","author":"Yeo Jinyoung","year":"2018","unstructured":"Jinyoung Yeo, Seung-won Hwang, Eunyee Koh, Nedim Lipka, 2018. Conversion prediction from clickstream: Modeling market prediction and customer predictability. IEEE Transactions on Knowledge and Data Engineering (2018)."},{"key":"e_1_3_2_1_47_1","unstructured":"Jiaxuan You Yichen Wang Aditya Pal Pong Eksombatchai Chuck Rosenburg and Jure Leskovec. 2019. Hierarchical temporal convolutional networks for dynamic recommender systems. In WWW."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Meizi Zhou Zhuoye Ding Jiliang Tang and Dawei Yin. 2018. Micro behaviors: A new perspective in e-commerce recommender systems. In WSDM.","DOI":"10.1145\/3159652.3159671"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Yu Zhu Hao Li Yikang Liao Beidou Wang Ziyu Guan Haifeng Liu and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM.. In IJCAI.","DOI":"10.24963\/ijcai.2017\/504"}],"event":{"name":"CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)","acronym":"CODS-COMAD 2024","location":"Bangalore India"},"container-title":["Proceedings of the 7th Joint International Conference on Data Science &amp; Management of Data (11th ACM IKDD CODS and 29th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632410.3632452","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632410.3632452","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:36:24Z","timestamp":1755869784000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632410.3632452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":50,"alternative-id":["10.1145\/3632410.3632452","10.1145\/3632410"],"URL":"https:\/\/doi.org\/10.1145\/3632410.3632452","relation":{},"subject":[],"published":{"date-parts":[[2024,1,4]]},"assertion":[{"value":"2024-01-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}