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M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 5 Issue 7, July 2016 | Popularity: 7.3 / 10

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Optimized Ranking Framework for Information Retrieval

Snehal Ukarande, Ashish Manwatkar

Abstract: Current era is of fast information retrieval. There are lots of research methodologies which are arising to give most fast and correct result set. This paper sheds light on fast and to the point information retrieval methodology for giving user with the most relevant document in secure way. Learning to rank is being progressively more trendy research area in the machine learning. Problem of ranking aims to encourage an ordering or inclination of relation among a set of instances. Learning to rank for information retrieval has gained a lot of interest in the recent years as ranking is the main problem in many information retrieval applications, like document retrieval, multimedia retrieval, text summarizing, collaborative filtering, question answering and online advertising machine translation etc. The large amount of the web documents makes it usually impracticable for the common users for finding their desired information by surfing over net. As a result, effective information retrieval is being more vital and also search engine has turned out to be important tool for people to search their required information.

Keywords: Ranking Model, Learning to Rank, Information Retrieval, Data Mining

Edition: Volume 5 Issue 7, July 2016

Pages: 1329 - 1332

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