Learning to rank

•

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This document discusses machine learning algorithms for ranking problems. It introduces supervised learning to rank methods including pointwise, pairwise and listwise approaches. Pointwise methods predict relevance scores independently but don't consider order. Pairwise approaches consider relative order but have high computational costs. Listwise methods aim to optimize entire orderings but have complexity issues. Practical challenges include defining objective metrics, generating training labels, and handling new items with limited data. Semi-supervised learning and matrix factorization can help address labeling problems.

Software

Learning to Rank
Overview
Introduction of machine learning algorithms

for ranking problem

Outline
• Introduction

• Ranking Methods

• Supervised Methods

• Numeric Methods

• Practical Issues

I want top php engineers
How to decide an order for these developers?

Ranking Everywhere
• Article ranking in community search page

• Article Inﬁnite Scroll

• Once the article opened, how to decide next 10 articles we recommend

• Applicant Quality Score

• How to select top quality applicants to review ﬁrst

• Codementor Request Matching

• …

Native Ranking Methods
• The ranking expression is human experience

• Need many time to tuning models

• Hard to scale out if number of features increases

• Overﬁtting problem

• Is it possible to use machine learning to solve ranking
problem?

Methods
Pointwise Approach
5
3
5
2
4 2
1

Methods
Pointwise Approach
5 5 4 3 2 2 1

Top Low

Methods
Pointwise Approach
• Model Complexity: O(|q|*n_q)

• |q|: number of queries

• n_q: number of matched documents of query q

• Algorithms

• SVM, McRank, SLR …

Methods
Pointwise Approach
• Pros

• Easy and intuitive to implement

• Fast modeling

• Cons

• Cannot consider ordering

• Labeling is hard

Methods
Pairwise Approach
Top Low
> > > > > >

Methods
Pairwise Approach
• Model Complexity: O(|q| * n^2)

• Algorithms

• RankNet, IR-SVM, RankBoost

Methods
Pairwise Approach
• Pros

• Consider relative order

• Most popular concept in real world

• Cons

• Need to generate O(n^2) tags, high cost

• Not consider the relevance between query and
document

Methods
Listwise Approach
Top Low
Which one is better ordering?

Methods
Listwise Approach
Best Order
One of the orders
Measure the diﬀerence with
best orders

Methods
Listwise Approach
• Model Complexity: O(|q| * n!)

• Many researches focus on reducing model complexity
by numerical methods

• Algorithms

• AdaRank, ListNet, SVM-MAP …

Methods
Listwise Approach
• Pros

• Consider all situations

• Based on the origin problem deﬁnition

• Cons

• O(n!) permutations (hard to implement and time bound)

• Hard to optimize (objective functions not always
continuous)

Practical Issues
• What is the objective metrics we pursue?

• How to generate labels?

• Cold-start problem

Objective Metrics
• Based on application

• Search

• Most page views

• Click Through Rate (CTR)

• CMX Job match

• Contract Conversion Rate (CCR)

• Interview Conversion Rate (ICR)

Labeling Problem
• How to label data if we have nothing

• Small dataset - human labeling is ok

• Large dataset

• Semi-supervised learning

• Matrix factorization

Labeling Problem
Semi-supervised
Iterative training with
relevance Labels

• Rule-based models

• Human ranking expressions
Train
Model
Predict
Scoring

Labeling Problem
Semi-supervised
Pseudo Labeling

Labeling Problem -
Matrix Factorization
• When data size is large, we can label part of pairs

P1 P2 P3 P4 P5
Php 1 2 5 ? 5
React 4 ? 1 3 1
Javascript 3 5 ? 1 ?
Lavarel ? 1 4 ? 5

Cold-start Problem
• Occurs in using some online metrics as features

• Click count

• View count

• How to handle new items?

• Find similar items based on static features

Reference
• Ranking

• http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.222.6261&rep=rep1&type=pdf

• http://www.shuang0420.com/2016/10/25/Search%20Engines%E7%AC%94%E8%AE%B0%20-
%20Learning%20to%20Rank/

• http://net.pku.edu.cn/~wbia/2011/slides/LEARNING%20TO%20RANK%20TUTORIAL%20-
%20tyliu.pdf

• https://people.cs.umass.edu/~jpjiang/cs646/16_learning_to_rank.pdf

• Semi-supervised learning techniques

• https://www.analyticsvidhya.com/blog/2017/09/pseudo-labelling-semi-supervised-learning-technique/

• https://datawhatnow.com/pseudo-labeling-semi-supervised-learning/

Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user's need. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this tutorial will be on the fundamentals of neural networks and their applications to learning to rank.

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Learning to rank

1. Learning to Rank Overview Introduction of machine learning algorithms for ranking problem

2. Outline • Introduction • Ranking Methods • Supervised Methods • Numeric Methods • Practical Issues

3. I want top php engineers

4. I want top php engineers How to decide an order for these developers?

5. Ranking Everywhere • Article ranking in community search page • Article Inﬁnite Scroll • Once the article opened, how to decide next 10 articles we recommend • Applicant Quality Score • How to select top quality applicants to review ﬁrst • Codementor Request Matching • …

6. Native Ranking System

7. Native Ranking Methods • The ranking expression is human experience • Need many time to tuning models • Hard to scale out if number of features increases • Overﬁtting problem • Is it possible to use machine learning to solve ranking problem?

8. Learning to Rank System

9. Supervised Methods

10. Methods Pointwise Approach 5 3 5 2 4 2 1

11. Methods Pointwise Approach 5 5 4 3 2 2 1 Top Low

12. Methods Pointwise Approach • Model Complexity: O(|q|*n_q) • |q|: number of queries • n_q: number of matched documents of query q • Algorithms • SVM, McRank, SLR …

13. Methods Pointwise Approach • Pros • Easy and intuitive to implement • Fast modeling • Cons • Cannot consider ordering • Labeling is hard

14. Methods Pairwise Approach > > > > > >

15. Methods Pairwise Approach Top Low > > > > > >

16. Methods Pairwise Approach • Model Complexity: O(|q| * n^2) • Algorithms • RankNet, IR-SVM, RankBoost

17. Methods Pairwise Approach • Pros • Consider relative order • Most popular concept in real world • Cons • Need to generate O(n^2) tags, high cost • Not consider the relevance between query and document

18. Numerical Methods

19. Methods Listwise Approach Top Low

20. Methods Listwise Approach Top Low Which one is better ordering?

21. Methods Listwise Approach Best Order One of the orders Measure the diﬀerence with best orders

22. Methods Listwise Approach • Model Complexity: O(|q| * n!) • Many researches focus on reducing model complexity by numerical methods • Algorithms • AdaRank, ListNet, SVM-MAP …

23. Methods Listwise Approach • Pros • Consider all situations • Based on the origin problem deﬁnition • Cons • O(n!) permutations (hard to implement and time bound) • Hard to optimize (objective functions not always continuous)

24. Practical Issues • What is the objective metrics we pursue? • How to generate labels? • Cold-start problem

25. Objective Metrics • Based on application • Search • Most page views • Click Through Rate (CTR) • CMX Job match • Contract Conversion Rate (CCR) • Interview Conversion Rate (ICR)

26. Labeling Problem • How to label data if we have nothing • Small dataset - human labeling is ok • Large dataset • Semi-supervised learning • Matrix factorization

27. Labeling Problem Semi-supervised Iterative training with relevance Labels • Rule-based models • Human ranking expressions Train Model Predict Scoring

28. Labeling Problem Semi-supervised Pseudo Labeling

29. Labeling Problem - Matrix Factorization • When data size is large, we can label part of pairs P1 P2 P3 P4 P5 Php 1 2 5 ? 5 React 4 ? 1 3 1 Javascript 3 5 ? 1 ? Lavarel ? 1 4 ? 5

30. Cold-start Problem • Occurs in using some online metrics as features • Click count • View count • How to handle new items? • Find similar items based on static features

31. Reference • Ranking • http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.222.6261&rep=rep1&type=pdf • http://www.shuang0420.com/2016/10/25/Search%20Engines%E7%AC%94%E8%AE%B0%20- %20Learning%20to%20Rank/ • http://net.pku.edu.cn/~wbia/2011/slides/LEARNING%20TO%20RANK%20TUTORIAL%20- %20tyliu.pdf • https://people.cs.umass.edu/~jpjiang/cs646/16_learning_to_rank.pdf • Semi-supervised learning techniques • https://www.analyticsvidhya.com/blog/2017/09/pseudo-labelling-semi-supervised-learning-technique/ • https://datawhatnow.com/pseudo-labeling-semi-supervised-learning/

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