User Modeling for the Result Re-Ranking in the Meta-Search Engines via Reinforcement Learning

Today there are thousands of search engines available, so it is difficult for users to know where they are, how to use them and what topics they best address. Meta-search engines reduce the users' burden by dispatching queries to multiple search engines in parallel and combining the returned results. But there are some problems yet. One of them is that, none of them includes the user model in the answer. In this paper, we propose a mechanism to create a mapping between different categories of users and the underlying search engines using the reinforcement learning approach. By this way, the meta-search engine learns to identify which search engines are most appropriate for particular queries from different user models.