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Sādhanā

, 44:97 | Cite as

Identification of efficient algorithms for web search through implementation of learning-to-rank algorithms

  • Nikhil DhakeEmail author
  • Shital Raut
  • Ashwini RahangdaleEmail author
Article
  • 32 Downloads

Abstract

Today, amount of information on the web such as number of publicly accessible web pages, hosts and web data is increasing rapidly and exhibiting an enormous growth at an exponential rate. Thus, information retrieval on web is becoming more difficult. Conventional methods of information retrieval are not very effective in ranking since they rank the results without automatically learning the model. Machine learning domain called learning-to-rank comes to the aid to rank the obtained results. Different state-of-the-art methodologies have been developed for learning-to-rank to date. This paper focuses on finding out the best algorithm for web search by implementation of different state-of-the-art algorithms for learning-to-rank. Our work in this paper marks the implementation of learning-to-rank algorithms and analyses effect of topmost performing algorithms on respective datasets. It presents an overall review on the approaches designed under learning-to-rank and their evaluation strategies.

Keywords

Information retrieval machine learning learning-to-rank 

References

  1. 1.
    Yoav F and Schapire R E 1997 A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1): 119–139MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baeza-Yates R 1999 Modern information retrieval. Pearson Education India, IndiaGoogle Scholar
  3. 3.
    Robertson S E 1997 Overview of the Okapi projects. J. Doc. 53: 3–7CrossRefGoogle Scholar
  4. 4.
    Ponte J M and Croft W B 1998 A language modeling approach to information retrieval. In: Proceedings of the Special Interest Group on Information Retrieval Conference, pp. 275–281Google Scholar
  5. 5.
    Page L, Brin S, Motwani R and Winograd T 1998 The pagerank citation ranking: bringing order to the web. Technical Report, Stanford Digital Library Technologies Project Google Scholar
  6. 6.
    Liu T Y 2011 Learning to Rank for Information Retrieval. Springer Science and Business MediaGoogle Scholar
  7. 7.
    Friedman J H 2001 Greedy function approximation: a gradient boosting machine. Technical Report, IMS Reitz Lecture, Stanford Google Scholar
  8. 8.
    Breiman L 2001 Random forests. Mach. Learn. 45: 5–32CrossRefGoogle Scholar
  9. 9.
    Thorsten J 2002 Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142Google Scholar
  10. 10.
    Freund Y, Iyer R, Schapire R and Singer Y 2003 An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4: 933–969MathSciNetzbMATHGoogle Scholar
  11. 11.
    Xu J and Li H 2007 AdaRank: a boosting algorithm for information retrieval. In: Proceedings of the Special Interest Group on Information Retrieval Conference, pp. 391–398Google Scholar
  12. 12.
    Cao Z, Qin T, Liu T Y, Tsai M and Li H 2007 Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the \(24^\text{th}\) International Conference on Machine Learning, ACM, pp. 129–136Google Scholar
  13. 13.
    Fen X, Liu T Y, Wang J, Zhang W and Li H 2008 Listwise approach to learning to rank: theory and algorithm. In: Proceedings of the 25th International Conference on Machine Learning, ACM, pp. 1192–1199Google Scholar
  14. 14.
    Chapelle O and Wu M 2010 Gradient descent optimization of smoothed information retrieval metrics. Inf. Retr. 13(3): 216–235CrossRefGoogle Scholar
  15. 15.
    Tao Q, Liu T Y and Li H 2010 A general approximation framework for direct optimization of information retrieval measures. Inf. Retr. 13(4): 375–397CrossRefGoogle Scholar
  16. 16.
    Taylor M, Guiver J, Robertson S and Minka T 2008 Softrank: optimizing non-smooth rank metrics. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, ACM, pp. 77–86Google Scholar
  17. 17.
    Xu J, Liu T Y, Lu M, Li H and Ma W Y 2008 Directly optimizing evaluation measures in learning to rank. In: Proceedings of the \(31^\text{ st }\) Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 107–114Google Scholar
  18. 18.
    Ou W, You Q, Mao X, Xia F, Yuan F and Hu W 2016 Listwise learning to rank by exploring structure of objects. IEEE Trans. Knowl. Data Eng. 28(7): 1934–1939CrossRefGoogle Scholar
  19. 19.
    Lawson C L and Hanson R J 1995 Solving least squares problems. Society for Industrial and Applied MathematicsGoogle Scholar
  20. 20.
    Wu Q, Burges C J C, Svore K and Gao J 2007 Adapting boosting for information retrieval measures. J. Inf. Retr. 13(3): 254–270CrossRefGoogle Scholar
  21. 21.
    Ibrahim O A S and Landa-Silva D 2017 Es-rank: evolution strategy learning to rank approach. In: Proceedings of the Symposium on Applied Computing, ACM, pp. 944–950Google Scholar
  22. 22.
    Yue Y, Finley T, Radlinski F and Joachims T 2007 A support vector method for optimizing average precision. In: Proceedings of the \(30^\text{ th }\) Annual International ACM SIGIR conference on Research and Development in Information Retrieval, ACM, pp. 271–278Google Scholar
  23. 23.
    Li P, Wu Q and Burges C J 2008 Mcrank: learning to rank using multiple classification and gradient boosting. In: Proceedings of Advances in Neural Information Processing Systems, pp. 897–904Google Scholar
  24. 24.
    Crammer K and Singer Y 2002 Pranking with ranking. In: Proceedings of Advances in Neural Information Processing Systems, pp. 641–647Google Scholar
  25. 25.
    Cossock D and Zhang T 2006 Subset ranking using regression. In: Proceedings of the International Conference on Computational Learning Theory, Springer, pp. 605–619Google Scholar
  26. 26.
    Shashua A and Levin A 2003 Ranking with large margin principle: two approaches. In: Proceedings of Advances in Neural Information Processing Systems, pp. 961–968Google Scholar
  27. 27.
    Chen K, Li R, Dou Y, Liang Z and Lv Q 2017 Ranking support vector machine with kernel approximation. Comput. Intell. Neurosci. Article ID 4629534Google Scholar
  28. 28.
    Burges C J C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N and Hullender G 2005 Learning to rank using gradient descent. In: Proceedings of the International Conference on Machine Learning, pp. 89–96Google Scholar
  29. 29.
    Burges C J, Ragno R and Le Q V 2007 Learning to rank with nonsmooth cost functions. Proc. Adv. Neural Inf. Process. Syst. 19:193–200Google Scholar
  30. 30.
    Metzler D and Bruce Croft W 2007 Linear feature-based models for information retrieval. Inf. Retr. 10(3): 257–274CrossRefGoogle Scholar
  31. 31.
    Wang Y, Wang L, Li Y, He D, Chen W and Liu T Y 2013 A theoretical analysis of NDCG ranking measures. In: Proceedings of the \(26^\text{ th }\) Annual Conference on Learning Theory (COLT 2013), vol. 8Google Scholar
  32. 32.
    Chapelle O, Metlzer D, Zhang Y and Grinspan P 2009 Expected reciprocal rank for graded relevance. In: Proceedings of the \(18^\text{ th }\) ACM Conference on Information and Knowledge Management, ACM, pp. 621–630Google Scholar
  33. 33.
    Pandey G, Ren Z, Wang S, Veijalainen J and de Rijke M 2018 Linear feature extraction for ranking. Inf. Retr. J. 21(6): 481–506CrossRefGoogle Scholar
  34. 34.
    Gigli A, Lucchese C, Nardini F M and Perego R 2016 Fast feature selection for learning to rank. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, ACM, pp. 167–170Google Scholar
  35. 35.
    Gupta P and Rosso P 2012 Expected divergence based feature selection for learning to rank. In: Proceedings of International Committee on Computational Linguistics 2012, pp. 431–440Google Scholar
  36. 36.
    Lai H J, Pan Y, Tang Y and Yu R 2013 Fsmrank: feature selection algorithm for learning to rank. IEEE Trans. Neural Netw. Learn. Syst. 24(6): 940–952CrossRefGoogle Scholar
  37. 37.
    Stone M 1977 An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society, Series B (Methodological) 39(1): 44–47MathSciNetzbMATHGoogle Scholar
  38. 38.
    Tao Q, Liu T Y, Xu J and Li H 2010 LETOR: a benchmark collection for research on learning to rank for information retrieval. Inf. Retr. 13(4): 346–374CrossRefGoogle Scholar
  39. 39.
    Tax N, Bockting S and Hiemstra D 2015 A cross-benchmark comparison of 87 learning to rank methods. Inf. Process. Manag. 51(6): 757–772CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVisvesvaraya National Institute of TechnologyNagpurIndia

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