models for metasearch
Given the ranked lists of documents returned by multiple search engines in response to a given query, the problem of metasearch is to combine these lists in a way which optimizes the performance of the combination. This paper makes three contributions to the problem of metasearch: (1) We describe and investigate a metasearch model based on an optimal democratic voting procedure, the Borda Count; (2) we describe and investigate a metasearch model based on Bayesian inference; and (3) we describe and investigate a model for obtaining upper bounds on the performance of metasearch algorithms. Our experimental results show that metasearch algorithms based on the Borda and Bayesian models usually outperform the best input system and are competitive with, and often outperform, existing metasearch strategies. Finally, our initial upper bounds demonstrate that there is much to learn about the limits of the performance of metasearch.
model metasearch documentation search engine response query combinational optimality performance paper voting algorithm experimentation input systems competition strategies demonstrate learning