2 research outputs found
Metasearch information fusion using linear programming
For a specific query merging the returned results from multiple search engines, in the
form of a metasearch aggregation, can provide significant improvement in the quality of
relevant documents. This paper suggests a minimax linear programming (LP) formulation for
fusion of multiple search engines results. The paper proposes a weighting method to
include the importance weights of the underlying search engines. This is a two-phase
approach which in the first phase a new method for computing the importance weights of the
search engines is introduced and in the second stage a minimax LP model for finding
relevant search engines results is formulated. To evaluate the retrieval effectiveness of
the suggested method, the 50 queries of the 2002 TREC Web track were utilized and
submitted to three popular Web search engines called Ask, Bing and Google. The returned
results were aggregated using two exiting approaches, three high-performance commercial
Web metasearch engines and our proposed technique. The efficiency of the generated lists
was measured using TREC-Style Average Precision (TSAP). The new findings demonstrate that
the suggested model improved the quality of merging considerably