Metasearch information fusion using linear programming

Abstract

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.

Voting-KEmeny Median Indicator Ranks Accordance method for determining criteria priority and weights in solving multi-attribute decision-making problems

“…The utilization of this approach, in addition to eliminating the use and not having to form a pairwise comparison matrix, whilst surveying the compatibility mechanism, requires the opinions of experts only once. Amin et al (2012) employed the determining of weights in preferential voting to amass the information in the Meta search engine. Soltanifar and Hosseinzadeh Lotfi (2011) utilized the hierarchical analysis process of voting to rank decision-making units in DEA.…”