A survey on Learning to Rank (LETOR) approaches in information retrieval

In Recent years, the application of machine learning approaches to conventional IR system evolve a new dimension in the field. The emphasis is now shifted from simply retrieving a set of documents to rank them also for a given query in terms of user´s need. The researcher´s task is not only to retrieve the documents from the corpus but also to rank them in order of their relevance to the user´s requirement. To improve the system´s performance is now the hot area of research. In this paper, an attempt has been made to put some of most commonly used algorithms in the community. It presents a survey on the approaches used to rank the retrieved documents and their evaluation strategies.