COPYRIGHT YEAR

2014

AUTHORS

Pranay Khattri, Love Rose Singh Sandhu, Piyush Rai, Shrimai Prabhumoye, S. Sowmya Kamath

TITLE

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

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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23 TRIPLES      19 PREDICATES      24 URIs      11 LITERALS

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2 sg:copyrightHolder Springer International Publishing Switzerland
3 sg:copyrightYear 2014
4 sg:ddsId Chap9
5 sg:doi 10.1007/978-3-319-07353-8_9
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14 sg:language En
15 sg:license http://scigraph.springernature.com/explorer/license/
16 sg:pageFirst 67
17 sg:pageLast 75
18 sg:scigraphId 3d7315ebd2655e6df9a0d58b40e01e1c
19 sg:title A Prototype of an Intelligent Search Engine Using Machine Learning Based Training for Learning to Rank
20 sg:webpage https://link.springer.com/10.1007/978-3-319-07353-8_9
21 rdf:type sg:BookChapter
22 rdfs:label BookChapter: A Prototype of an Intelligent Search Engine Using Machine Learning Based Training for Learning to Rank
23 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-319-07353-8_9
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