Metasearch aggregation using linear programming and neural networks

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)351-366
Number of pages16
JournalInternational Journal of Operational Research
Volume33
Issue number3
DOIs
Publication statusPublished - Jan 1 2018

Fingerprint

Linear programming
Neural networks
Search engine
Metasearch
Methodology
Network model
Query

Keywords

  • Data aggregation
  • Linear programming
  • Metasearch
  • Neural networks
  • Search engine

ASJC Scopus subject areas

  • Management Science and Operations Research

Cite this

Metasearch aggregation using linear programming and neural networks. / Sharma, Sujeet; Govindaluri, Srikrishna; Amin, Gholam R.

In: International Journal of Operational Research, Vol. 33, No. 3, 01.01.2018, p. 351-366.

Research output: Contribution to journalArticle

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