Utilizing query performance predictors for early termination in meta-search

Şener, Emre
In the context of web, a meta-search engine is a system that forwards an incoming user query to all the component search engines (aka, resources); and then merges the retrieved results. Given that hundreds of such resources may exist, it is mandatory for a meta-search engine to avoid forwarding a query to all available resources, but rather focus on a subset of them. In this thesis, we first introduce a novel incremental query forwarding strategy for meta-search. More specifically, given a ranked list of N search engines, our strategy operates in rounds, such that in each round, we retrieve the results of the next k “unvisited” resources in the list (where k<N), asses the quality of the intermediate merged list, and stop if any further quality improvement seems unlikely. As our second contribution, we introduce a novel incremental query result merging strategy. In this strategy, we forward query to all search engines but we assess the quality of intermediate merged lists as early as we retrieve the results from an engine and stop if any further quality improvements are not likely. In order to assess the result quality, we utilize post-retrieval query performance prediction (QPP) techniques. Our experiments using the standard FedWeb 2013 dataset reveal that the proposed strategies can reduce the response time and/or network bandwidth usage, while the quality of the result is comparable to, or sometimes, even better than the baseline strategy.
Citation Formats
E. Şener, “Utilizing query performance predictors for early termination in meta-search,” M.S. - Master of Science, Middle East Technical University, 2016.