Multi task learning for boosting with application to web search ranking olivier chapelle et al
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Multi-Task Learning for Boosting with Application to Web Search Ranking Olivier Chapelle et al. Presenter: Wei Cheng. Outline. Motivation Backgrounds Algorithm From svm to boosting using L1 regularization Є -boosting for optimization Overall algorithm Evaluation

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Multi-Task Learning for Boosting with Application to Web Search Ranking Olivier Chapelle et al.

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Multi task learning for boosting with application to web search ranking olivier chapelle et al

Multi-Task Learning for Boosting with Application to Web Search RankingOlivier Chapelle et al.

Presenter: Wei Cheng


Outline

Outline

  • Motivation

  • Backgrounds

  • Algorithm

    • From svm to boosting using L1 regularization

    • Є-boosting for optimization

    • Overall algorithm

  • Evaluation

  • Overall review and new research points discussion


Motivation

Motivation

Domain specific engine is better!

e.g. ‘gelivable’ (very useful)

Different/same search engine(s) for different countries?


Motivation1

Motivation

  • Should we train ranking model separately?

    • Corps in some domains might be too small to train a good model

    • Solution:

Multi-task learning


Backgrounds

Backgrounds

SinnoJialin Pan and Qiang Yang, TKDE’2010


Backgrounds1

Backgrounds

SinnoJialin Pan and Qiang Yang, TKDE’2010


Backgrounds2

Backgrounds

  • Why transfer learning works?

SinnoJialin Pan et al. At WWW’10


Backgrounds3

Backgrounds

  • Why transfer learning works?(continue)


Backgrounds4

Backgrounds

  • Why transfer learning works?(continue)


Backgrounds5

Backgrounds

traditional learning

Input:

LearnerA

LearnerB

Target:

Dog/human

Girl/boy


Backgrounds6

Backgrounds

Multi-task learning

Input:

Joint Learning Task

Target:

Dog/human

Girl/boy


Algorithm

Algorithm

  • The algorithm aims at designing an algorithm based on gradient boosted decision trees

    • Inspired by svm based multi-task solution and boosting-trick.

  • Using Є-boosting for optimization


Algorithm1

Algorithm

  • From svm to boosting using L1 regularization

    • Previous svm based multi-task learning:


Algorithm2

Algorithm

  • Svm(kernel-trick)---boosting (boosting trick)

Pick set of non-linear functions(e.g., decision trees, regression trees,….)

Apply every single function to each data point

H

Xф(X)

|H|=J


Boosting for optimization

Є-boosting for optimization

  • Using L1 regulization

Using Є-boosting


Algorithm3

Algorithm


Evaluation

Evaluation

  • Datasets


Evaluation1

Evaluation


Evaluation2

Evaluation


Evaluation3

Evaluation


Evaluation4

Evaluation


Evaluation5

Evaluation


Overall review and new research point discussion

Overall review and new research point discussion

  • Contributions:

    • Propose a novel multi-task learning method based on gradient boosted decision tree, which is useful for web-reranking applications. (e.g., personalized search).

    • Have a thorough evaluation on we-scale datasets.

  • New research points:

    • Negative transfer:

    • Effective grouping: flexible domain adaptation


Multi task learning for boosting with application to web search ranking olivier chapelle et al

Q&A

Thanks!


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