Title: Metasearch aggregation using linear programming and neural networks

Authors: Sujeet Kumar Sharma; Srikrishna Madhumohan Govindaluri; Gholam R. Amin

Addresses: Department of Operations Management and Business Statistic, College of Economics and Political Science, Sultan Qaboos University, Oman ' Department of Operations Management and Business Statistic, College of Economics and Political Science, Sultan Qaboos University, Oman ' Department of Operations Management and Business Statistic, College of Economics and Political Science, Sultan Qaboos University, Oman

Abstract: A metasearch engine aggregates the retrieved results of multiple search engines for a submitted query. The purpose of this paper is to formulate a metasearch aggregation using linear programming and neural networks by incorporating the importance weights of the involved search engines. A two-stage methodology is introduced where the importance weights of individual search engines are determined using a neural network model. The weights are then used by a linear programming model for aggregating the final ranked list. The results from the proposed method are compared with the results obtained from a simple model that assumes subjective weights for search engines. The comparison of the two sets of results shows that neural network-based linear programming model is superior in optimising the relevance of aggregated results.

Keywords: metasearch; search engine; data aggregation; linear programming; neural networks.

DOI: 10.1504/IJOR.2018.095625

International Journal of Operational Research, 2018 Vol.33 No.3, pp.351 - 366

Received: 19 Jun 2015
Accepted: 07 Nov 2015
Published online: 16 Oct 2018 *

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