Ranking Methods in Machine Learning

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1 Ranking Methods in Machine Learning A Tutorial Introduction Shivani Agarwal Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology

2 Example 1: Recommendation Systems

3 Example 2: Information Retrieval

4 Example 2: Information Retrieval information

5 Example 2: Information Retrieval information

6 Example 3: Drug Discovery Problem: Millions of structures in a chemical library. How do we identify the most promising ones?

7 Example 4: Bioinformatics Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, nonmendelian diseases... Nature 405: , 2000

8 Example 4: Bioinformatics With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of nonmendelian disorders based on large-scale genomewide studies... Nature 405: , 2000

9 Types of Ranking Problems Instance Ranking Label Ranking Subset Ranking Rank Aggregation?

10 Instance Ranking doc1 > doc2, 10 doc3 > doc5, 20

11 Label Ranking doc1 doc2 sports > politics health > money science > sports money > politics

12 Subset Ranking > query 1 doc1 > doc2, doc3 doc5 query 2 doc2 > doc4, doc11 > doc3

13 Rank Aggregation query 1 results of search engine 1 results of search engine 2 desired ranking query 2 results of search engine 1 results of search engine 2 desired ranking

14 Types of Ranking Problems Instance Ranking Label Ranking Subset Ranking Rank Aggregation? This tutorial

15 Tutorial Road Map Part I: Theory & Algorithms Bipartite Ranking k-partite Ranking Ranking with Real-Valued Labels General Instance Ranking RankSVM RankBoost RankNet Part II: Applications Applications to Bioinformatics Applications to Drug Discovery Subset Ranking and Applications to Information Retrieval Further Reading & Resources

16 Part I Theory & Algorithms [for Instance Ranking]

17 Bipartite Ranking Relevant (+) doc1+ doc2+ doc3+ Irrelevant (-) doc1- doc2- doc3- doc4-

18 Bipartite Ranking

19 Bipartite Ranking

20 Is Bipartite Ranking Different from Binary Classification?

21 Is Bipartite Ranking Different from Binary Classification?

22 Is Bipartite Ranking Different from Binary Classification?

23 Is Bipartite Ranking Different from Binary Classification?

24 Is Bipartite Ranking Different from Binary Classification?

25 Is Bipartite Ranking Different from Binary Classification?

26 Bipartite Ranking: Basic Algorithmic Framework

27 Bipartite RankSVM Algorithm [Herbrich et al, 2000; Joachims, 2002; Rakotomamonjy, 2004]

28 Bipartite RankSVM Algorithm

29 Bipartite RankBoost Algorithm [Freund et al, 2003]

30 Bipartite RankBoost Algorithm

31 Bipartite RankNet Algorithm [Burges et al, 2005]

32 k-partite Ranking Rating k doc1 k doc2 k doc3 k Rating 2 doc1 2 doc2 2 doc3 2 doc4 2 Rating 1 doc1 1 doc2 1 doc3 1 doc4 1

33 k-partite Ranking

34 k-partite Ranking: Basic Algorithmic Framework

35 Ranking with Real-Valued Labels doc1 y1 doc2 y2 doc3 y3

36 Ranking with Real-Valued Labels

37 Ranking with Real-Valued Labels: Basic Algorithmic Framework

38 General Instance Ranking doc1 > doc1, r1 doc2 > doc2, r2

39 General Instance Ranking r i

40 General Instance Ranking

41 General Instance Ranking: Basic Algorithmic Framework

42 General RankSVM Algorithm [Herbrich et al, 2000; Joachims, 2002]

43 General RankBoost Algorithm [Freund et al, 2003]

44 General RankNet Algorithm [Burges et al, 2005]

45 Tutorial Road Map Part I: Theory & Algorithms Bipartite Ranking k-partite Ranking Ranking with Real-Valued Labels General Instance Ranking RankSVM RankBoost RankNet Part II: Applications Applications to Bioinformatics Applications to Drug Discovery Subset Ranking and Applications to Information Retrieval Further Reading & Resources

46 Part II Applications [and Subset Ranking]

47 Application to Bioinformatics Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, nonmendelian diseases... Nature 405: , 2000

48 Application to Bioinformatics With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of nonmendelian disorders based on large-scale genomewide studies... Nature 405: , 2000

49 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data

50 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data

51 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data

52 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data

53 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data

54 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data

55 Formulation as a Bipartite Ranking Problem Relevant Not relevant

56 Microarray Gene Expression Data Sets [Golub et al, 1999; Alon et al, 1999]

57 Selection of Training Genes

58 Top-Ranking Genes for Leukemia Returned by RankBoost [Agarwal & Sengupta, 2009]

59 Biological Validation [Agarwal et al, 2010]

60 Application to Drug Discovery Problem: Millions of structures in a chemical library. How do we identify the most promising ones?

61 Formulation as a Ranking Problem with Real-Valued Labels

62 Cheminformatics Data Sets [Sutherland et al, 2004]

63 DHFR Results Using RankSVM [Agarwal et al, 2010]

64 Application to Information Retrieval (IR)

65 Learning to Rank in IR

66 Learning to Rank in IR

67 Learning to Rank in IR

68 Learning to Rank in IR

69 Learning to Rank in IR

70 Learning to Rank in IR

71 Learning to Rank in IR

72 Learning to Rank in IR

73 Learning to Rank in IR

74 Learning to Rank in IR

75 General Subset Ranking > query 1 doc1 > doc2, doc3 doc5 query 2 doc2 > doc4, doc11 > doc3

76 General Subset Ranking

77 Subset Ranking with Real-Valued Relevance Labels query 1 doc1 y1, doc2 y2, doc3 y3 query 2 doc1 y1, y2, y3 doc2 doc3

78 Subset Ranking with Real-Valued Relevance Labels

79 RankSVM Applied to IR/Subset Ranking Standard RankSVM [Joachims, 2002]

80 RankSVM Applied to IR/Subset Ranking RankSVM with Query Normalization & Relevance Weighting [Agarwal & Collins, 2010; also Cao et al, 2006]

81 Ranking Performance Measures in IR Mean Average Precision (MAP)

82 Ranking Performance Measures in IR Normalized Discounted Cumulative Gain (NDCG)

83 Ranking Algorithms for Optimizing MAP/NDCG

84 LETOR 3.0/OHSUMED Data Set [Liu et al, 2007]

85 OHSUMED Results NDCG

86 OHSUMED Results MAP

87 Further Reading & Resources [Incomplete!]

88 Early Papers on Ranking W. W. Cohen, R. E. Schapire, and Y. Singer, Learning to order things, Journal of Artificial Intelligence Research, 10: , R. Herbrich, T. Graepel, and K. Obermayer, Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, T. Joachims, Optimizing search engines using clickthrough data, KDD Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4: , C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, G. Hullender, Learning to rank using gradient descent, ICML 2005.

89 Generalization Bounds for Ranking S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, D. Roth, Generalization bounds for the area under the ROC curve, Journal of Machine Learning Research, 6: , S. Agarwal, P. Niyogi, Generalization bounds for ranking algorithms via algorithmic stability, Journal of Machine Learning Research, 10: , C. Rudin, R. Schapire, Margin-based ranking and an equivalence between AdaBoost and RankBoost, Journal of Machine Learning Research, 10: , 2009

90 Bioinformatics/Drug Discovery Applications S. Agarwal and S. Sengupta, Ranking genes by relevance to a disease, CSB S. Agarwal, D. Dugar, and S. Sengupta, Ranking chemical structures for drug discovery: A new machine learning approach. Journal of Chemical Information and Modeling, DOI /ci , 2010.

91 Other Applications Natural Language Processing M. Collins and T. Koo, Discriminative reranking for natural language parsing, Computational Linguistics, 31:25 69, Collaborative Filtering M. Weimer, A. Karatzoglou, Q. V. Le, and A. Smola, CofiRank - Maximum margin matrix factorization for collaborative ranking, NIPS Manhole Event Prediction C. Rudin, R. Passonneau, A. Radeva, H. Dutta, S. Ierome, and D. Isaac, A process for predicting manhole events in Manhattan, Machine Learning, DOI /s , 2010.

92 IR Ranking Algorithms Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Hunag, and H.W. Hon, Adapting ranking SVM to document retrieval, SIGIR C.J.C. Burges, R. Ragno, and Q.V. Le, Learning to rank with non-smooth cost functions. NIPS J. Xu and H. Li, AdaRank: A boosting algorithm for information retrieval. SIGIR Y. Yue, T. Finley, F. Radlinski, and T. Joachims, A support vector method for optimizing average precision. SIGIR M. Taylor, J. Guiver, S. Robertson, T. Minka, Softrank: optimizing non-smooth rank metrics. WSDM 2008.

93 IR Ranking Algorithms S. Chakrabarti, R. Khanna, U. Sawant, and C. Bhattacharyya, Structured learning for nonsmooth ranking losses. KDD D. Cossock and T. Zhang, Statistical analysis of Bayes optimal subset ranking, IEEE Transactions on Information Theory, 54: , T. Qin, X.D. Zhang, M.F. Tsai, D.S. Wang, T.Y. Liu, and H. Li. Query-level loss functions for information retrieval. Information Processing and Management, 44: , O. Chapelle and M. Wu, Gradient descent optimization of smoothed information retrieval metrics. Information Retrieval (To appear), S. Agarwal and M. Collins, Maximum margin ranking algorithms for information retrieval, ECIR 2010.

94 NIPS Workshop 2005 Learning to Rank SIGIR Workshops Learning to Rank for Information Retrieval NIPS Workshop 2009 Advances in Ranking American Institute of Mathematics Workshop in Summer 2010 The Mathematics of Ranking

95 Tutorial Articles & Books Tie-Yan Liu, Learning to Rank for Information Retrieval, Foundations & Trends in Information Retrieval, Shivani Agarwal, A Tutorial Introduction to Ranking Methods in Machine Learning, In preparation. Shivani Agarwal (Ed.), Advances in Ranking Methods in Machine Learning, Springer-Verlag, In preparation.

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