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Metasearch aggregation using linear programming and neural networks

Author

Listed:
  • Sujeet Kumar Sharma
  • Srikrishna Madhumohan Govindaluri
  • Gholam R. Amin

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.

Suggested Citation

  • Sujeet Kumar Sharma & Srikrishna Madhumohan Govindaluri & Gholam R. Amin, 2018. "Metasearch aggregation using linear programming and neural networks," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 33(3), pages 351-366.
    • Handle: RePEc:ids:ijores:v:33:y:2018:i:3:p:351-366
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