Expected Loss Optimization for Document Ranking by Active Learning

Authors(2) :-G Saranya, M Manikandan

Learning to rank is the emerging research field in many data mining applications and information retrieval techniques (e.g. Search engines). The major issue in ranking algorithm is that the quality or ranking is affected by labeled examples, since it is very expensive and also time consuming to collect labeled samples. This problem brings a great need for active learning algorithm; however, in literature learning to rank uses supervised learning algorithm where ranking is based on labeled data only. A general active learning framework Balanced two stage Expected Loss Optimization is proposed to select the most informative document based on user’s query. The algorithm is based on two levels, Query level and Document level and grade distribution is done based on query and document pairs. Experiment on web search dataset has demonstrated with the proposed algorithm.

Authors and Affiliations

G Saranya
Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
M Manikandan
Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India

Expected Loss Optimization, Active Learning, Ranking And Supervised Learning

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Publication Details

Published in : Volume 2 | Issue 2 | March-April 2016
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 56-66
Manuscript Number : IJSRSET162195
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

G Saranya, M Manikandan, " Expected Loss Optimization for Document Ranking by Active Learning, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.56-66, March-April-2016.
Journal URL : http://ijsrset.com/IJSRSET162195

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