Ranking Models and Learning to Rank: A Survey

Abstract

Current era is of fastest information retrieval. There are lots of research methodologies which are arising to give the fastest and correct result set. This paper mainly focuses on survey of the ranking models and learning to rank technique for giving the effective and efficient information retrieval. Learning to rank is flattering progressively more trendy research area in machine learning. The problem of ranking aims to encourage an ordering or inclination of relation among a set of instances in the input space. Learning to rank for information retrieval has gained a lot of interest in the recent years as ranking is the central problem in many information retrieval applications, like document retrieval, multimedia retrieval, text summarization, collaborative filtering, question answering and online advertising machine translation etc. The tremendously large size of the Web documents makes it usually impracticable for the common users for finding their desired information by surfing the net. As a result, effective and efficient information retrieval is being more important and also search engine (information retrieval system) has turned out to be a vital tool for people to locate their needed information.