Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
