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Learning To Rank data2day 2017

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Stefan Kühn
Stefan Kühn

The document discusses learning to rank, which involves using machine learning techniques to generate ordered rankings based on training data. It describes ranking as a supervised learning problem that deals with ordinal labels rather than categorical or real-valued labels. The document outlines different approaches to learning to rank, including pointwise, pairwise, and listwise methods. It also provides examples of applications like search engine rankings and personalized ad recommendations.

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Learning to Rank Stefan Kühn Join me on XING data2day Heidelberg - September 28th, 2017 Stefan Kühn (XING) Ranking 28.09.2017 1 / 30
Contents 1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 2 / 30
1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 3 / 30
Rankings in Everyday Life TODO Lists Prioritized Backlogs Top X songs/movies/. . . You get the idea. . . Stefan Kühn (XING) Ranking 28.09.2017 4 / 30
Rankings in History It all started with Stefan Kühn (XING) Ranking 28.09.2017 5 / 30
Rankings Nowadays German States by Employee Happiness (according to Kununu) Stefan Kühn (XING) Ranking 28.09.2017 6 / 30
Rankings, Heuristics, Decisions Rankings are about comparisons Rankings are about decision-making Some heuristics are about both Recognition Heuristic If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion. proposed by Gigerenzer and Goldstein, built upon the great works of Kahneman and Tversky Stefan Kühn (XING) Ranking 28.09.2017 7 / 30
1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 8 / 30
Learning Is Ranking a Machine Learning Problem? Stefan Kühn (XING) Ranking 28.09.2017 9 / 30
Machine Learning Concepts Supervised - Learning from Labels Figure out how to generate correct labels using the given data Classification Regression Unsupervised - Learning from Data Identify hidden/inherent structure using the given data Clustering Dimensionality Reduction / Manifold Learning Outlier Detection Stefan Kühn (XING) Ranking 28.09.2017 10 / 30
Supervised versus Unsupervised Learning to Rank Figure out how to generate good ranking using the given data What about Learning to Rank = Machine-Learned Ranking or MLR? 1 Supervised because ranks are like labels? 2 Unsupervised because ranks are typically based on implicit feedback, i.e. latent/hidden/inherent structure? 3 Mixed/intermediate/something else? 4 Ill-posed question? Could you please rank these options according to whatever you think is appropriate? Stefan Kühn (XING) Ranking 28.09.2017 11 / 30
Supervised versus Unsupervised Learning to Rank Figure out how to generate good ranking using the given data What about Learning to Rank = Machine-Learned Ranking or MLR? 1 Supervised because ranks are like labels? 2 Unsupervised because ranks are typically based on implicit feedback, i.e. latent/hidden/inherent structure? 3 Mixed/intermediate/something else? 4 Ill-posed question? Could you please rank these options according to whatever you think is appropriate? And by the way, how did you do it? Stefan Kühn (XING) Ranking 28.09.2017 11 / 30
Example: XING Stream How to order News? By time? By content/topic? By popularity? By clicking probability? Every choice changes the problem to solve while the result set is always the same - a ranked list of items. Every choice represents a different distance measure / objective function to minimize. Stefan Kühn (XING) Ranking 28.09.2017 12 / 30
1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 13 / 30
Ranking - Problem Formulation Items x ∈ X Ordered Labels or Ranks 1 > 2 > . . . > k > . . . Ranking rule f that allows to do the following: Input: Unordered subset {x, y, z, . . .} ⊆ X Output: Ordered list, i.e. y > z > x > . . . Example: Text search Items: Set of Documents Ranking rule f : Similarity measure for documents and search terms Stefan Kühn (XING) Ranking 28.09.2017 14 / 30
Ranking and Level of Measurement Supervised Learning Problems Classification - Nominal Scale - Class Labels Ranking - Ordinal Scale - Ranks Regression - Intervall Scale - Real Values Ranking is the task of predicting labels on an ordinal scale. Informally: Learn ordering from labeled training data - typically ordered lists of items - and try to predict ordering for new sets of items. What is special about this? Ordering is context-dependent. One additional item (or one item less) can change all other ranks. This is clearly different compared to regression and classification. Stefan Kühn (XING) Ranking 28.09.2017 15 / 30
Ranking in Information Retrieval CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=518546 Stefan Kühn (XING) Ranking 28.09.2017 16 / 30
Ranking - Pointwise Approach Characteristics Input: Single items Evaluation: Scoring function evaluated for each point/item Optimization: Loss function derived from individual scores Reduces Ranking Problem to either Regression Classification Ordinal Regression Stefan Kühn (XING) Ranking 28.09.2017 17 / 30
Ranking - Pointwise Image taken from Tie-Yan Liu @ WWW 2009 Tutorial on Learning to Rank http://wwwconference.org/www2009/pdf/T7A-LEARNING TO RANK TUTORIAL.pdf Stefan Kühn (XING) Ranking 28.09.2017 18 / 30
Ranking - Pointwise Problems with the Pointwise Approach Length of item lists can differ significantly Example: There are more website related to the search term Online (ca. 10 Mrd.) than to Offline (ca. 666 Mio) Position of items on list is not taken into account Example: Incorrect ordering of the top 10 results will have a slightly bigger impact than errors/inversions below position 123456789 Consequence Longer lists will dominate the optimization, while actually the shorter lists are more important for humans/customers. Advantages If all individual scores are known, all possible Rankings are determined. Stefan Kühn (XING) Ranking 28.09.2017 19 / 30
Ranking - Pairwise Approach Characteristics Input: Pairs of Items Evaluation: Preference function evaluated for each pair - binary classification Optimization: Pairwise Classification Loss derived from all pairings, weighted majority voting Reduces Ranking Problem to Binary (or pairwise) Classification Stefan Kühn (XING) Ranking 28.09.2017 20 / 30
Ranking - Pairwise Image taken from Tie-Yan Liu @ WWW 2009 Tutorial on Learning to Rank http://wwwconference.org/www2009/pdf/T7A-LEARNING TO RANK TUTORIAL.pdf Stefan Kühn (XING) Ranking 28.09.2017 21 / 30
Ranking - Pairwise Problems with the Pairwise Approach Length of item lists can differ significantly Number of pairs depends quadratically on the length of the list Even bigger imbalance w.r.t. list length Advantages Comparisons of pairs of elements is a much more natural approach to Ranking than Regression or Classification. Stefan Kühn (XING) Ranking 28.09.2017 22 / 30
Ranking - Listwise Approach Characteristics Input: Set of Items Evaluation: Some Evaluation Metric Optimization: Either: Directly minimize Evaluation Metric Or: Loss function defined for permutations of the given input Reduces Ranking Problem to either Direct Optimization of Evaluation Metric Listwise Loss Optimization (Distance between lists is non-trivial) Stefan Kühn (XING) Ranking 28.09.2017 23 / 30
Ranking - Listwise Image taken from Tie-Yan Liu @ WWW 2009 Tutorial on Learning to Rank http://wwwconference.org/www2009/pdf/T7A-LEARNING TO RANK TUTORIAL.pdf Stefan Kühn (XING) Ranking 28.09.2017 24 / 30
Ranking - Listwise Problems with the Listwise Approach Huge complexity issue Direct Optimization: Non-smooth functions Often only incomplete knowledge about ground truth for lists (only tiny subset available for learning) Advantages Positions on lists are visible to the algorithms. Stefan Kühn (XING) Ranking 28.09.2017 25 / 30
Important Contributions Natural Language Processing tf-idf Okapi BM25 Link to Information Theory Interesting Nonlinear Evaluation Metrics P@k = Precision restricted to the best k items MAP Discounted Cumulative Gain = DCG Interesting Non-Standard Ojective Functions (N)DCG as optimization objective non-continuous and non-smooth Interesting Rankers Pointwise: Subset Ranking; McRank; PRanking (Ordinal Regression) Pairwise: RankNet; FRank; RankBoost; Ranking SVM Listwise: SoftRank; SoftNDCG; SVM-MAP, Structural SVM, AdaRank Stefan Kühn (XING) Ranking 28.09.2017 26 / 30
1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 27 / 30
Example: Personalized Ad Recommendations Standard Approaches Contextual Bandits Policies based on classifiers for each ad Collaborative Filtering Based on Latent Features, e.g. when using Matrix Factorization Main Problem Extreme sparsity of positive feedback Stefan Kühn (XING) Ranking 28.09.2017 28 / 30
Example: Personalized Ad Recommendations New Approaches Still Contextual Bandits Policies based on rankers instead of classifiers Recent Paper by Chaudhuri et al. Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem Works best in the case of extreme sparsity Stefan Kühn (XING) Ranking 28.09.2017 29 / 30
Thank you! Stefan Kühn (XING) Ranking 28.09.2017 30 / 30

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Learning To Rank data2day 2017

  • 1. Learning to Rank Stefan Kühn Join me on XING data2day Heidelberg - September 28th, 2017 Stefan Kühn (XING) Ranking 28.09.2017 1 / 30
  • 2. Contents 1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 2 / 30
  • 3. 1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 3 / 30
  • 4. Rankings in Everyday Life TODO Lists Prioritized Backlogs Top X songs/movies/. . . You get the idea. . . Stefan Kühn (XING) Ranking 28.09.2017 4 / 30
  • 5. Rankings in History It all started with Stefan Kühn (XING) Ranking 28.09.2017 5 / 30
  • 6. Rankings Nowadays German States by Employee Happiness (according to Kununu) Stefan Kühn (XING) Ranking 28.09.2017 6 / 30
  • 7. Rankings, Heuristics, Decisions Rankings are about comparisons Rankings are about decision-making Some heuristics are about both Recognition Heuristic If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion. proposed by Gigerenzer and Goldstein, built upon the great works of Kahneman and Tversky Stefan Kühn (XING) Ranking 28.09.2017 7 / 30
  • 8. 1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 8 / 30
  • 9. Learning Is Ranking a Machine Learning Problem? Stefan Kühn (XING) Ranking 28.09.2017 9 / 30
  • 10. Machine Learning Concepts Supervised - Learning from Labels Figure out how to generate correct labels using the given data Classification Regression Unsupervised - Learning from Data Identify hidden/inherent structure using the given data Clustering Dimensionality Reduction / Manifold Learning Outlier Detection Stefan Kühn (XING) Ranking 28.09.2017 10 / 30
  • 11. Supervised versus Unsupervised Learning to Rank Figure out how to generate good ranking using the given data What about Learning to Rank = Machine-Learned Ranking or MLR? 1 Supervised because ranks are like labels? 2 Unsupervised because ranks are typically based on implicit feedback, i.e. latent/hidden/inherent structure? 3 Mixed/intermediate/something else? 4 Ill-posed question? Could you please rank these options according to whatever you think is appropriate? Stefan Kühn (XING) Ranking 28.09.2017 11 / 30
  • 12. Supervised versus Unsupervised Learning to Rank Figure out how to generate good ranking using the given data What about Learning to Rank = Machine-Learned Ranking or MLR? 1 Supervised because ranks are like labels? 2 Unsupervised because ranks are typically based on implicit feedback, i.e. latent/hidden/inherent structure? 3 Mixed/intermediate/something else? 4 Ill-posed question? Could you please rank these options according to whatever you think is appropriate? And by the way, how did you do it? Stefan Kühn (XING) Ranking 28.09.2017 11 / 30
  • 13. Example: XING Stream How to order News? By time? By content/topic? By popularity? By clicking probability? Every choice changes the problem to solve while the result set is always the same - a ranked list of items. Every choice represents a different distance measure / objective function to minimize. Stefan Kühn (XING) Ranking 28.09.2017 12 / 30
  • 14. 1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 13 / 30
  • 15. Ranking - Problem Formulation Items x ∈ X Ordered Labels or Ranks 1 > 2 > . . . > k > . . . Ranking rule f that allows to do the following: Input: Unordered subset {x, y, z, . . .} ⊆ X Output: Ordered list, i.e. y > z > x > . . . Example: Text search Items: Set of Documents Ranking rule f : Similarity measure for documents and search terms Stefan Kühn (XING) Ranking 28.09.2017 14 / 30
  • 16. Ranking and Level of Measurement Supervised Learning Problems Classification - Nominal Scale - Class Labels Ranking - Ordinal Scale - Ranks Regression - Intervall Scale - Real Values Ranking is the task of predicting labels on an ordinal scale. Informally: Learn ordering from labeled training data - typically ordered lists of items - and try to predict ordering for new sets of items. What is special about this? Ordering is context-dependent. One additional item (or one item less) can change all other ranks. This is clearly different compared to regression and classification. Stefan Kühn (XING) Ranking 28.09.2017 15 / 30
  • 17. Ranking in Information Retrieval CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=518546 Stefan Kühn (XING) Ranking 28.09.2017 16 / 30
  • 18. Ranking - Pointwise Approach Characteristics Input: Single items Evaluation: Scoring function evaluated for each point/item Optimization: Loss function derived from individual scores Reduces Ranking Problem to either Regression Classification Ordinal Regression Stefan Kühn (XING) Ranking 28.09.2017 17 / 30
  • 19. Ranking - Pointwise Image taken from Tie-Yan Liu @ WWW 2009 Tutorial on Learning to Rank http://wwwconference.org/www2009/pdf/T7A-LEARNING TO RANK TUTORIAL.pdf Stefan Kühn (XING) Ranking 28.09.2017 18 / 30
  • 20. Ranking - Pointwise Problems with the Pointwise Approach Length of item lists can differ significantly Example: There are more website related to the search term Online (ca. 10 Mrd.) than to Offline (ca. 666 Mio) Position of items on list is not taken into account Example: Incorrect ordering of the top 10 results will have a slightly bigger impact than errors/inversions below position 123456789 Consequence Longer lists will dominate the optimization, while actually the shorter lists are more important for humans/customers. Advantages If all individual scores are known, all possible Rankings are determined. Stefan Kühn (XING) Ranking 28.09.2017 19 / 30
  • 21. Ranking - Pairwise Approach Characteristics Input: Pairs of Items Evaluation: Preference function evaluated for each pair - binary classification Optimization: Pairwise Classification Loss derived from all pairings, weighted majority voting Reduces Ranking Problem to Binary (or pairwise) Classification Stefan Kühn (XING) Ranking 28.09.2017 20 / 30
  • 22. Ranking - Pairwise Image taken from Tie-Yan Liu @ WWW 2009 Tutorial on Learning to Rank http://wwwconference.org/www2009/pdf/T7A-LEARNING TO RANK TUTORIAL.pdf Stefan Kühn (XING) Ranking 28.09.2017 21 / 30
  • 23. Ranking - Pairwise Problems with the Pairwise Approach Length of item lists can differ significantly Number of pairs depends quadratically on the length of the list Even bigger imbalance w.r.t. list length Advantages Comparisons of pairs of elements is a much more natural approach to Ranking than Regression or Classification. Stefan Kühn (XING) Ranking 28.09.2017 22 / 30
  • 24. Ranking - Listwise Approach Characteristics Input: Set of Items Evaluation: Some Evaluation Metric Optimization: Either: Directly minimize Evaluation Metric Or: Loss function defined for permutations of the given input Reduces Ranking Problem to either Direct Optimization of Evaluation Metric Listwise Loss Optimization (Distance between lists is non-trivial) Stefan Kühn (XING) Ranking 28.09.2017 23 / 30
  • 25. Ranking - Listwise Image taken from Tie-Yan Liu @ WWW 2009 Tutorial on Learning to Rank http://wwwconference.org/www2009/pdf/T7A-LEARNING TO RANK TUTORIAL.pdf Stefan Kühn (XING) Ranking 28.09.2017 24 / 30
  • 26. Ranking - Listwise Problems with the Listwise Approach Huge complexity issue Direct Optimization: Non-smooth functions Often only incomplete knowledge about ground truth for lists (only tiny subset available for learning) Advantages Positions on lists are visible to the algorithms. Stefan Kühn (XING) Ranking 28.09.2017 25 / 30
  • 27. Important Contributions Natural Language Processing tf-idf Okapi BM25 Link to Information Theory Interesting Nonlinear Evaluation Metrics P@k = Precision restricted to the best k items MAP Discounted Cumulative Gain = DCG Interesting Non-Standard Ojective Functions (N)DCG as optimization objective non-continuous and non-smooth Interesting Rankers Pointwise: Subset Ranking; McRank; PRanking (Ordinal Regression) Pairwise: RankNet; FRank; RankBoost; Ranking SVM Listwise: SoftRank; SoftNDCG; SVM-MAP, Structural SVM, AdaRank Stefan Kühn (XING) Ranking 28.09.2017 26 / 30
  • 28. 1 Rankings and Humans 2 Ranking and Machine Learning 3 Formalizing Ranking Problems 4 Rankings and Recommender Systems Stefan Kühn (XING) Ranking 28.09.2017 27 / 30
  • 29. Example: Personalized Ad Recommendations Standard Approaches Contextual Bandits Policies based on classifiers for each ad Collaborative Filtering Based on Latent Features, e.g. when using Matrix Factorization Main Problem Extreme sparsity of positive feedback Stefan Kühn (XING) Ranking 28.09.2017 28 / 30
  • 30. Example: Personalized Ad Recommendations New Approaches Still Contextual Bandits Policies based on rankers instead of classifiers Recent Paper by Chaudhuri et al. Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem Works best in the case of extreme sparsity Stefan Kühn (XING) Ranking 28.09.2017 29 / 30
  • 31. Thank you! Stefan Kühn (XING) Ranking 28.09.2017 30 / 30