Abstract:
Distributed heterogeneous search systems are an emerging phenomenon in Web search, in which independent topic-specific search engines provide search services, and metasearchers distribute user's queries to only the most suitable search engines. Previous research has investigated methods for engine selection and merging of search results (i.e. performance improvements from the user's perspective). We focus instead on performance from the service provider's point of view (e.g, income from queries processed vs. resources used to answer them). We consider a scenario in which individual search engines compete for user queries by choosing which documents (topics) to index. The difficulty here stems from the fact that the utilities of local engine actions should depend on the uncertain actions of competitors. Thus, naive strategies (e.g, blindly indexing lots of popular documents) are ineffective. We model the competition between search engines as a stochastic game, and propose a reinforcement learning approach to managing search index contents. We evaluate our approach using a large log of user queries to 47 real search engines.
