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

  1. Bo Long and Jiang Bian, "Active Learning for Ranking through Expected Loss Optimization", IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No.5, 2015.
  2. Martin Szummer, "Semi-supervised learning to Rank with Preference Regularization", in Proc. 32nd Int ACM SIGIR Conf. Res. Develop. Inform. Retrieval,pp. 662–663, 2011.
  3. Qian B. Li H. Wang J. Wang X. and Davidson I, "Active Learning to Rank using Pairwise Supervision", Proc. 13th SIAM Int. Conf. Data Mining, pp. 297–305, 2013.
  4. Wenpu Xing and Ghorbani Ali, "Weighted Page Rank", Proceedings of the IEEE International Conference on Computer Science, 2004.
  5. Xia F. Liu T. Wang J. Zhang W. and Li H, "List wise approach to learning to rank: theory and algorithm", Proc. 25th Int. Conf. Mach. Learn., pp. 1192–1199, 2008.
  6. Khaled Alsabti Sanjay Ranka and Vineet Singh,"Efficient Information Retrieval Using Dynamic Page Rank Algorithm", In Proceedings of IPPS/SPDP Workshop on High Performance Data Mining, 2011.
  7. Guiver J. and Snelson E, ‘Learning to rank with SoftRank and Gaussian processes’, Proc. 31st Ann. Int. ACM SIGIR Conf. Res.Develop. Inform. Retrieval,pp. 259–266, 2008.
  8. R.Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technology behind Search, 2nd Addison-Wesley Professional, 2011.
  9. C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008.
  10. L.Yang, L. Wang, B. Geng, and X.-S.Hua. "Query sampling for ranking learning in web search". In SIGIR'09: Proceedings of the 32nd international ACMSIGIR conference on Research and development in information retrieval, pages 754-755, New York, NY, USA, 2009.
  11. C.Campbell, N. Cristianini, and A. Smola. "Query learning with large margin classifiers". In Proceedings of the Seventeenth International Conference on Machine Learning, pages 111-118. Morgan Kaufmann, 2000.
  12. D. Cossock and T. Zhang, "Subset ranking using regression" .In Proc. Conf. on Learning Theory, 2006.
  13. E. Yilmaz and S. Robertson,"Deep versus shallow judgments in learning to rank". In SIGIR '09:Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 662-663, New York, NY, USA, 2009.
  14. A. M. ZarehBidoki and N. Yazdani, "DistanceRank: An intelligent ranking algorithm for webpages" information Processing and Management, Vol 44, No. 2, pp. 877-892, 2008.
  15. Rekha Jain, DrG.N.Purohit, "Page Ranking Algorithms for Web Mining", International Journal of Computer application,Vol 13, Jan 2011.
  16. Xu J. and Li H., "AdaRank: A boosting algorithm for information retrieval", SIGIR, pp. 391–398, 2007

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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