Ranking a set of documents based on their relevances with respect to a given query is a central problem of information retrieval (IR). Traditionally people have been using unsupervised scoring methods like tf-idf, BM25, Language Model etc., but recently supervised machine learning framework is being used successfully to learn a ranking function, which is called learning-to-rank (LtR) problem. There are a few surveys on LtR in the literature; but these reviews provide very little assistance to someone who, before delving into technical details of different algorithms, wants to have a broad understanding of LtR systems and its evolution from and relation to the traditional IR methods. This chapter tries to address this gap in the literature. Mainly the following aspects are discussed: the fundamental concepts of IR, the motivation behind LtR, the evolution of LtR from and its relation to the traditional methods, the relationship between LtR and other supervised machine learning tasks, the general issues pertaining to an LtR algorithm, and the theory of LtR.
Parallel learning to rank for information retrieval