User profiles, descriptions of use interests, can be used by search engines to provide personalized search results. Many approaches to creating user profiles collect user information through proxy servers (to capture browsing histories) or desktop bots (to capture activities on a personal computer). Both these techniques require participation of the user to install the proxy server or bot. In a previous study [1], we explored the use of a less-invasive means of gathering user information for personalized search. In particular, we built user profiles based on activity at the search site itself and studied the use of these profiles to provide personalized search results. By implementing a wrapper around the Google search engine, we were able to collect information about individual user search activities. More specifically, we collected the queries for which at least one search result was examined, and the snippets (titles and summaries) for each examined result. User profiles were created by classifying the collected information (queries or snippets) into concepts in a reference concept hierarchy. These profiles were then used to re-rank the search results and the rank-order of the user-examined results before and after re-ranking were compared. Our study found that user profiles based on queries were as effective as those based on snippets. We also found that our personalized re-ranking resulted in a 34% improvement in the rank-order of the user-selected results.

In this demo we present misearch (http://moby.ittc.ku.edu/⁓micro/demo), a meta-search engine that implemented the algorithm analyzed in the previous study.