Abstract
The explosive growth of the Internet has made information on the web large and complicated. If the structure of a website is not optimized, users could easily get lost and could not find the most important information at the first time. The adaptive website can present the information that users needed by analyzing the users’ behavior. However, visitors may have different needs at different times. Most of recommended methods are not considerate of dynamic or time-dependent needs. This paper presents a recommender system based on reinforcement learning. We assume that five parameters are on recommendation, which include clicks of the page, time that spent on viewing the page, paths to find the page, hierarchy of the page, and the rank of the page. With the help of reinforcement learning to adjust the weight of five parameters, we aim to reduce the paths that user needed to find the object page.
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References
Schwarzkopf E (2001) An Adaptive web site for the UM2001 conference. In: Proceedings of the UM2001 workshop on machine learning for user modeling, p 77–86
Perkowitz M, Etzioni O (1997) Adaptive web sites: an AI challenge. In: Proceedings of international joint conferences on artificial intelligence, Nagoya
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge
Catledge L, Pitkow J (1995) Characterizing browsing behaviors on the World Wide Web. In: Proceedings of computer networks and ISDN systems, vol 27(6)
Srivastava J, Cooley R, Deshpande M, Tan P-T (2000) Web usage mining: discovery and applications of usage patterns from web data. In Proceedings of SIGKDD explorations, vol 1(2), pp 1–12
Perkowitz M, Etzioni O (1998) Adaptive web sites: automatically synthesizing web pages. In: Proceedings of association for the advancement of artificial intelligence, Madison, pp 727–732
Brusilovsky P (1996) Methods and techniques of adaptive hypermedia. User Model User-Adap Inter J 6:87–129
Pierrakos D, Paliouras G, Papatheodorou C, Spyropoulos CD (2003) Web usage mining as a tool for personalization: a survey. In Proceedings of user modeling and user-adapted interaction, vol 13, pp 311–372
TeĂeni D, Feldman R (2001) Performance and satisfaction in aadaptive websites: an experiment on searches within a task-adapted website. J Assoc Inf Syst, 2(3):1–30
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Tang, YL., Ting, IH., Wang, SL. (2013). Website Navigation Recommendation Based on Reinforcement Learning Technique. In: Uden, L., Wang, L., Hong, TP., Yang, HC., Ting, IH. (eds) The 3rd International Workshop on Intelligent Data Analysis and Management. Springer Proceedings in Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7293-9_10
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DOI: https://doi.org/10.1007/978-94-007-7293-9_10
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