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Learning to Rank

Learning to Rank. From Pairwise Approach to Listwise Approach. Agenda. Introduction Ranking Problem Ranking Pairwise Ranking (Brief) Listwise Ranking Probability Models Permutation Probability Top one Probability Results. Introduction. Construct model or method that learns to rank

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Learning to Rank

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  1. Learning to Rank From Pairwise Approach to Listwise Approach

  2. Agenda • Introduction • Ranking Problem • Ranking • Pairwise Ranking (Brief) • Listwise Ranking • Probability Models • Permutation Probability • Top one Probability • Results

  3. Introduction • Construct model or method that learns to rank • Area of use: • Anti Spam • Product Rating • Expert Finding • ...

  4. d_1^i d_2^i d_3^i . . d_n^i Introduction • Ranking Problem – Document retrieval Documents: {d_1, d_2, ... d_n} Ranking of documents Ranking System Query: q

  5. Query Q D = {d , d , ...., d } 1 2 n Instance : (d , d ) 1 2 Pairwise Ranking • Classification of objects Relevance label Classification Model

  6. Pairwise Ranking • Support Vector Machine • Ranking SVM • Boosting • RankBoost • Neural Network • RankNet

  7. m (i) (i) L (y , z ) i=1 Listwise Ranking • Training (1) (m) Q = {q , ...., q } Queries d = {d , ...., d } Documents y = {y , ...., y } Judgements x = {x , ...., x } Features f (x ) Score Func. z = {f(x ) , ...., f(x )} Scores (i) (i) (i) (i) n 1 (i) (i) (i) (i) m (i) (i) 1 n T = {(x , y )} (i) i=1 (i) (i) (i) 1 n (i) j Listwise loss function (i) (i) (i) (i) n 1

  8. Listwise Ranking • Ranking • New Query : q • Associated Docs. : d • Feature vectors : x • Trained rank. Func. : f (x ) • Rank documents in descending order ( i’ ) ( i’ ) ( i’ ) ( i’ ) j’

  9. Probability of pi given s o(S ) o(S ) n Pi(j) Pi(k) P (pi) = s n j=1 k=j Permutation Probability • f : s probability distribution pi = <pi(1), pi(2), ...., pi(n)> s = (s , s , .... s ) 1 2 n

  10. Top one prob. of j P (j) = P (p). s s P(1)=j,p n Top one Probability • Probability of being ranked on top of list

  11. f = f x = f (x ) z (f ) = {f (x ), ..., f (x )} w (i) (i) w j j (i) (i) (i) w w 1 1 ListNet • Optimizing loss function • Neural Network as model • Gradient Descent as optimization alg. w = neural network

  12. Results • TREC • Web pages from .gov domain • OSHUMED • Documents and queries on medicine • CSearch • Data from commerciel search engine

  13. Results • NDCG – Normalized Discounted Cumulative Gain • Relevance judgements > 2 • Korrekt – Delvist korrekt - Ukorrekt • MAP – Mean Average Precision • Relevance judgements = 2 • Korrekt - Ukorrekt

  14. Results • NDCG on TREC

  15. Results • NDCG on OSHUMED

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