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.
Published Version
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