Personal recommendation based on a user's understanding

Abstract

Current web personalization mainly focuses on extracting web pages interesting to users. However, difficulty in understanding web contents often arises for some users mainly due to the lack of background knowledge on the contents. As a solution to this problem, this paper proposes a method of personalizing web search that produces search results suitable to the understanding level of a user. Fuzzy sets and membership functions are defined in order to keep adjusting the level of a user, which is updated based on the levels of web pages on which the user shows preferences. Extensive simulation experiments are conducted to tune the system parameters optimally and to show that the proposed system outperforms not only a general search engine but also a system that updates the understanding level of a user with the mean of the web page levels. © 2009 Wiley Periodicals, Inc.