Adaptive recommendation for preferred information and browsing action based on web-browsing behavior

Authors:
Kosuke Takano;Kin Fun Li
Affiliations:
Department of Information S Computer Sciences, Kanagawa Institute of Technology, Kanagawa, Japan;Department of Electrical S Computer Engineering, University of Victoria, Victoria, BC, Canada
Venue:
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Year:
2010

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Abstract

A Web recommender system based on the inference from a user’s Web-browsing behavior has been proposed and implemented. This system is capable of recommending items of interest to a user and specific Web-browsing action on the current item using a novel similarity measure approach. The recommender is adaptive to individual user’s preference as well as a user’s changing interest via a dynamic user feedback mechanism and empirical statistics on Web-browsing actions taken. Furthermore, users’ quantitative comments and the qualitative measures of users’ behavior provide an ideal setting to ascertain the premise, implicitly used in several other existing recommender systems, that there is a correlation between preference information and browsing behavior.