Abstract
As the Web becomes more and more dynamic, it is interesting to explore the short-term modelling of its user behaviour. Nowadays, it is important to have an information about user’s preferences and needs online. It allows us, in addition to other advantages, also to predict user’s future actions. In this paper we describe the doctoral research focused on the modelling of the short-term changes in user’s behaviour. We explore the task of user session exit intent prediction. Our approach employs generally available data sources on user behaviour on the Web, so it is domain independent.
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References
Herder, E.: An Analysis of User Behavior on the Web - Understanding the Web and its Users. VDM Verlag, Saarbrücken (2007)
Kassak, O., Kompan, M., Bielikova, M.: Student behavior in a web-based educational system: exit intent prediction. Eng. Appl. Artif. Intell. J. 51, 136–149 (2016). Issue Mining the Humanities: Technologies and Applications, Elsevier
Kassak, O., Kompan, M., Bielikova, M.: Personalized hybrid recommendation for group of users: top-n multimedia recommender. Inform. Process. Manage. J. 52(3), 459–477 (2016). Elsevier
Kompan, M., Bielikova, M.: Group recommendations: survey and perspectives. Comput. Inform. 33(2), 1–31 (2014)
Kosala, R., Blockeel, H.: Web mining research: a survey. ACM SIGKDD Explor. Newsl. 2(1), 1–15 (2000)
Kukar-Kinney, M., Close, A.G.: The determinants of consumers’ online shopping cart abandonment. J. Acad. Mark. Sci. 38(2), 240–250 (2010)
Mills, C., Bosch, N., Graesser, A., D’Mello, S.: To quit or not to quit: predicting future behavioral disengagement from reading patterns. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 19–28. Springer, Heidelberg (2014)
Wang, W., Zhao, D., Luo, H., Wang, X.: Mining user interests in web logs of an online news service based on memory model. In: IEEE 8th International Conference on Networking, Architecture and Storage, pp. 151–155 (2013)
Wojewnik, P., Kaminski, B., Zawisza, M., Antosiewicz, M.: Social-network influence on telecommunication customer attrition. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2011. LNCS, vol. 6682, pp. 64–73. Springer, Heidelberg (2011)
Tan, M., Shao, P.: Prediction of student dropout in e-Learning program through the use of machine learning method. Int. J. Emerg. Tech. Learn. 10(1), 11–17 (2015)
Zhou, B., Zhang, B., Liu, Y., Xing, K.: User model evolution algorithm: forgetting and reenergizing user preference. In: International Conference on IoT and 4th International Conference on Cyber, Physical and Social Computing, pp. 444–447 (2011)
Acknowledgement
This work is partially supported the grants APVV-15-0508, VG 1/0646/15 and it is the partial result of the Research and Development Operational Programme for the project No. ITMS 26240120039 co-funded by the ERDF and STU Grant scheme for Support of Young Researchers.
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Kassak, O., Kompan, M., Bielikova, M. (2016). Short-term User Behaviour Changes Modelling. In: Ivanović, M., et al. New Trends in Databases and Information Systems. ADBIS 2016. Communications in Computer and Information Science, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-44066-8_23
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DOI: https://doi.org/10.1007/978-3-319-44066-8_23
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