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A Prototype of an Intelligent Search Engine Using Machine Learning Based Training for Learning to Rank

  • Piyush Rai
  • Shrimai Prabhumoye
  • Pranay Khattri
  • Love Rose Singh Sandhu
  • S. Sowmya Kamath
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

Learning to Rank is a concept that focuses on the application of supervised or semi-supervised machine learning techniques to develop a ranking model based on training data. In this paper, we present a learning based search engine that uses supervised machine learning techniques like selection based and review based algorithms to construct a ranking model. Information retrieval techniques are used to retrieve the relevant URLs by crawling the Web in a Breadth-First manner, which are then used as training data for the supervised and review based machine learning techniques to train the crawler. We used the Gradient Descent Algorithm to compare the two techniques and for result analysis.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Piyush Rai
    • 1
  • Shrimai Prabhumoye
    • 1
  • Pranay Khattri
    • 1
  • Love Rose Singh Sandhu
    • 1
  • S. Sowmya Kamath
    • 1
  1. 1.Department of Information TechnologyNational Institute of Technology KarnatakaSurathkalIndia

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