Using Ontology-Based User Preferences to Aggregate Rank Lists in Web Search

This paper studies rank aggregation by using ontology-based user preferences in the context of Web search. We introduce a set of techniques to combine the respective rank lists produced by different attributes of user preferences. Furthermore, the learned user preferences are structured as a taxonomic hierarchy (a simple ontology). We use the learned ontology to store the attributes such as, the topics that a user is interested in and the degrees of user interests in these topics. The primary goal of our work is to form a broadly acceptable rank list among these attributes by making use of rank-based aggregation. Experiment results on a real click-through data set show that our user-centered rank aggregation techniques are effective in improving the quality of the Web search in terms of user satisfaction.