Identification of efficient algorithms for web search through implementation of learning-to-rank algorithms

Abstract

Today, amount of information on the web such as number of publicly accessible web pages, hosts and web data is increasing rapidly and exhibiting an enormous growth at an exponential rate. Thus, information retrieval on web is becoming more difficult. Conventional methods of information retrieval are not very effective in ranking since they rank the results without automatically learning the model. Machine learning domain called learning-to-rank comes to the aid to rank the obtained results. Different state-of-the-art methodologies have been developed for learning-to-rank to date. This paper focuses on finding out the best algorithm for web search by implementation of different state-of-the-art algorithms for learning-to-rank. Our work in this paper marks the implementation of learning-to-rank algorithms and analyses effect of topmost performing algorithms on respective datasets. It presents an overall review on the approaches designed under learning-to-rank and their evaluation strategies.