practical lessons from predicting clicks on ads at facebook

Download Practical Lessons from Predicting Clicks on Ads at Facebook

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Practical Lessons from PredictingClicks on Ads at Facebook2014/1/27 (Tue.)Chang Wei-Yuan @ MakeLab Lab Meeting

Facebook ADKDD14+1OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought2+Introduction3+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought4+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought5+6

+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought7+Decision tree feature transforms8+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought9+Logistic regression for linear classifierStochastic Gradient Descent (SGD) algorithm

the tunable parameters are optimized by grid searchBayesian online learning scheme for profit regression10

+One advantages of LR over BOPR is that the model size is halfthe smaller model size may lead to better cache locality and thus faster cache lookup11

+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought12+Data freshnessClick prediction systems are often deployed in dynamic environments where the data distribution changes over time. 13+14

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These findings indicate that it is worth retraining on a daily basis. +Batchone option would be to have a recurring daily job that retrains the models, possibly in batchConcurrencythe training can be done via concurrency in a multi-core machine with large amount of memory

16+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought17+Online data joinerThe boosted decision trees can be trained daily, but the linear classier can be trained in near real-time by online learning.Online Joinergenerates real-time training data used to train the linear classifier via online learning18+Online data joinerThe boosted decision trees can be trained daily, but the linear classier can be trained in near real-time by online learning.Online Joinergenerates real-time training data used to train the linear classifier via online learningHow to label for a new instance ? 19+Online data joinerThe boosted decision trees can be trained daily, but the linear classier can be trained in near real-time by online learning.Online Joinergenerates real-time training data used to train the linear classifier via online learningperform a distributed stream-to-stream join on ad impressions and ad clicks

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+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought22+ExperimentNumber of boosting trees23

+ExperimentBoosting feature importance24

+ExperimentBoosting feature importance25

+ExperimentHistorical features26

+ExperimentHistorical features27

+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought28+ConclusionThis has inspired a promising hybrid model architecture for click prediction.boosted decision trees and a linear classieronline learning method with real-time training data29+OutlineIntroductionMethodDecision tree feature transformsLogistic regression for linear classifierData freshnessOnline data joinerExperimentConclusionThought30+Thanks for listening.2014 / 1 / 27 (Tue.) @ MakeLab Lab Meetingv123582@gmail.com

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