Aggregating user-centered rankings to improve web search
Lin Li, Zhenglu Yang, Masaru Kitsuregawa · 2007
This paper is to investigate rank aggregation based on multi-ple user-centered measures in the context of the web search. We introduce a set of techniques to combine ranking lists in order of user interests termed as a user profile. Moreover, based on the click-history data, a kind of taxonomic hierar-chy automatically models the user profile which can include a variety of attributes of user interests. We mainly focus on the topics a user is interested in and the degrees of user in-terests in these topics. The primary goal of our work is to form a broadly acceptable ranking list, rather than that deter-mined by an individual ranking measure. Experiment results on a real click-history data set show the effectiveness of our aggregation techniques to improve the web search.