Knowledge of users’ preferences are of high value for every e-commerce website. It can be used to improve customers’ loyalty by presenting personalized products’ recommendations. A user’s interest in a particular product can be estimated by observing his or her behaviors. Implicit methods are less accurate than the explicit ones, but implicit observation is done without interruption of having to give ratings for viewed items. This article presents results of e-commerce customers’ preference identification study. During the study the author’s extension for FireFox browser was used to collect participants’ behavior and preference data. Based on them over thirty implicit indicators were calculated. As a final result the decision tree model for prediction of e-customer products preference was build.
Avery, C. & Zeckhauser, R. (1997, March). Recommender systems for evaluating ComputerMessages. Communications of the ACM, Vol. 40, Issue 3.
Claypool, M., Le, P., Wased, M. & Brown, D. (2001). Implicit interest indicators. In Proc. 6th International Conference on Intelligent User Interfaces.
Cooper, M.D. & Chen, H.-M. (2001). Predicting the Relevance of a Library Catalog Search, Journal of the American Society for Information Science, 52 (10).
Jung, K. (2001). Modeling web user interest with implicit indicators, Master Thesis, Florida Institute of Technology, USA.
Kelly, D. (2005). Implicit Feedback: Using Behavior to Infer Relevance. New directions in cognitive information retrieval, The Information Retrieval Series, Vol. 19, Section IV.
Kelly, D. & Belkin, N.J. (2001). Reading time, scrolling and interaction: exploring implicitsources of user preferences for relevance feedback. In SIGIR ’01.
Kim, H. & Chan, P.K. (2008). Implicit Indicators For Interesting Web Pages. Proceedings of International Conference on Web Information Systems and Technologies, Miami.
Kim, K., Carroll, J.M. & Rosson, M. (2002). An Empirical Study of Web Personalization Assistants:Supporting End-Users in Web Information Systems. In Proceedings of the IEEE 2002 Symposia on Human Centric Computing Languages and Environments. Arlington, USA.
Kim, Y.S., Yum, B.J., Song J. & Kim, S.M. (2005). Development of a recommender systembased on navigational and behavioral patterns of customers in e-commerce sites. Expert Systems with Applications Vol. 28, Issue 2.
Middleton, S.E., Shadbolt, N.R. & De Roure, D.C. (2003). Capturing Interest through Inferenceand Visualization: Ontological User Profiling in Recommender Systems. In Proceedings of the Second Annual Conference on Knowledge Capture.
Miller, B.N., Riedl, J.T. & Konstan, J.A. (2003). GroupLens for Usenet: Experiences in ApplyingCollaborative Filtering to a Social Information System. In: C. Lueg, D. Fisher [Eds.], From Usenet to CoWebs: Interacting With Social Information Spaces. London: Springer Press.
Morita, M. & Shinoda, Y. (1994). Information Filtering Based on User Behavior Analysis andBest Match Text Retrieval, In Proceedings of ACM Conference on Research and Development in Information Retrieval (SIGIR ’94), Dublin, Ireland.
Nichols, D.M. (1997). Implicit Ratings and Filtering. In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, Hungary.
Oard, D.W. & Kim, J. (2001). Modeling Information Content Using Observable Behavior. In Proceedings of the 64th Annual Meeting of the American Society for Information Science and Technology (ASIST ’01), USA.
Seo, Y.W. & Zhang, B.T. (2000). A Reinforcement Learning Agent for Personalized In-formationFiltering, In Proceedings of the 5th International Conference on Intelligent User Interfaces, USA.
Velayathan, G. & Yamada, S. (2005). Behavior Based Web Page Evaluation, Journal of WebEngineering, Vol. 1, No. 1.