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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

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

Presenter: Wei Cheng


Outline
Outline Search Ranking

  • 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 Search Ranking

Domain specific engine is better!

e.g. ‘gelivable’ (very useful)

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


Motivation1
Motivation Search Ranking

  • 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 Search Ranking

SinnoJialin Pan and Qiang Yang, TKDE’2010


Backgrounds1
Backgrounds Search Ranking

SinnoJialin Pan and Qiang Yang, TKDE’2010


Backgrounds2
Backgrounds Search Ranking

  • Why transfer learning works?

SinnoJialin Pan et al. At WWW’10


Backgrounds3
Backgrounds Search Ranking

  • Why transfer learning works?(continue)


Backgrounds4
Backgrounds Search Ranking

  • Why transfer learning works?(continue)


Backgrounds5
Backgrounds Search Ranking

traditional learning

Input:

LearnerA

LearnerB

Target:

Dog/human

Girl/boy


Backgrounds6
Backgrounds Search Ranking

Multi-task learning

Input:

Joint Learning Task

Target:

Dog/human

Girl/boy


Algorithm
Algorithm Search Ranking

  • 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 Search Ranking

  • From svm to boosting using L1 regularization

    • Previous svm based multi-task learning:


Algorithm2
Algorithm Search Ranking

  • 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 Search Ranking-boosting for optimization

  • Using L1 regulization

Using Є-boosting


Algorithm3
Algorithm Search Ranking


Evaluation
Evaluation Search Ranking

  • Datasets


Evaluation1
Evaluation Search Ranking


Evaluation2
Evaluation Search Ranking


Evaluation3
Evaluation Search Ranking


Evaluation4
Evaluation Search Ranking


Evaluation5
Evaluation Search Ranking


Overall review and new research point discussion
Overall review and new research point discussion Search Ranking

  • 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 Search Ranking

Thanks!