Metasearch aggregation using linear programming and neural networks

Sujeet Sharma, Srikrishna Govindaluri, Gholam R. Amin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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
Issue number3
Publication statusPublished - 2018


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

ASJC Scopus subject areas

  • Management Science and Operations Research


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