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. © Springer International Publishing Switzerland 2014.