multi-task learning for boosting with application to web search ranking olivier chapelle et al
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Multi-Task Learning for Boosting with Application to Web Search RankingOlivier Chapelle et al.
Presenter: Wei Cheng
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
Different/same search engine(s) for different
countries?
Domain specific engine is better!e.g. ‘gelivable’ (very useful)
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
Sinno Jialin Pan and Qiang Yang, TKDE’2010
Backgrounds
Sinno Jialin Pan and Qiang Yang, TKDE’2010
Backgrounds
• Why transfer learning works?
Sinno Jialin Pan et al. At WWW’10
Backgrounds
• Why transfer learning works?(continue)
Backgrounds
• Why transfer learning works?(continue)
Backgrounds
LearnerA
Input:
Target:
LearnerB
Dog/human Girl/boy
traditional learning
Backgrounds
Joint Learning Task
Input:
Target: Dog/human Girl/boy
Multi-task learning
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
Algorithm
• From svm to boosting using L1 regularization• Previous svm based multi-task learning:
Algorithm
• Svm(kernel-trick)---boosting (boosting trick)
Pick set of non-linear functions(e.g., decision trees,
regression trees,….)
H|H|=J
Apply every single function to each data point
1( ).
( ).( )J
h x
x
h x
Xф(X)
Є-boosting for optimization
• Using L1 regulization
Using Є-boosting
Algorithm
Evaluation
• Datasets
Evaluation
Evaluation
Evaluation
Evaluation
Evaluation
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
Q&A
Thanks!