Post on 15-Jan-2015
description
Yandex Relevance Prediction ChallengeOverview of “CLL” team’s solution
R. Gareev1, D. Kalyanov2, A. Shaykhutdinova1, N. Zhiltsov1
1Kazan (Volga Region) Federal University
210tracks.ru
28 December 2011
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Problem statement
I Predict document relevance from user behavior a.k.a«Implicit Relevance Feedback»
I See also http://imat-relpred.yandex.ru/en formore details
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User session example
RegionQ1 ⇒ 1 2 3 4 5 T = 0
3 T = 105 T = 351 T = 100
Q2 ⇒ 6 7 8 9 10 T = 1306 T = 1509 T = 170
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Labeled data
Given judgements for some pairs of documents and queries:
I a document Dj is relevant for a query Qi from aregion Ror
I a document Dj is not relevant for a query Qi from aregion R
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The problem
I Given a set Q of search queries, for each (q, R) ∈ Qprovide a sorted list of documents D1, . . . , Dm that arerelevant to q in the region R
I Area Under the ROC Curve (AUC) averaged over allthe test query-pairs is the target evaluation metric
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AUC score
I Consider list of documents: D1, . . . , Di︸ ︷︷ ︸Prefix of length i
, . . . Dm
I (FPR(i),TPR(i)) gives a single point in ROC curveI AUC is the area under ROC curveI AUC = Probability that randomly chosen relevant document
come before randomly chosen non-relevand document8 / 52
Our problem restatement
I We consider it as a machine learning taskI Using relevance judgements, learn a classifierH(R,Q,D) that predicts that document D is relevantto a query Q from a region R
I Replace RegionID, QueryID and DocumentID withrelated features extracted from click log
I Use the classifier H(R,Q,D) to compute a list, sortedw.r.t. classifier’s certainty scores, for a query Q from aregion R
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Features
I A «feature» is a function of (Q,R,D).I Each feature is associated/not associated with itsrelated region
TypesI Document featuresI Query featuresI Time-concerned features
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Document features
1 (Q,D)→ Number of occurences of an URL in the SERP list2 (Q,D)→ Number of clicks3 (Q,D)→ Click-through rate4 (Q,D)→ Average position in the click sequence5 (Q,D)→ Average rank in the SERP list6 (Q,D)→ Average rank in the SERP list when URL is clicked7 (Q,D)→ Probability of being last clicked8 (Q,D)→ Probability of being first clicked
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User session example
RegionQ1 ⇒ 1 2 3 4 5 T = 0
3 T = 105 T = 351 T = 100
Q2 ⇒ 6 7 8 9 10 T = 1306 T = 1509 T = 170
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Query features
1 (Q)→ Average number of clicks in subsession2 (Q)→ Probability of being rewritten (being not last query insession)
3 (Q)→ Probability of being resolved (probability of its resultsbeing last clicked)
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User session example
RegionQ1 ⇒ 1 2 3 4 5 T = 0
3 T = 105 T = 351 T = 100
Q2 ⇒ 6 7 8 9 10 T = 1306 T = 1509 T = 170
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Time-concerned features
1 (Q)→ Average time to first click2 (Q,D)→ Average time spent reading a document D
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User session example
RegionQ1 ⇒ 1 2 3 4 5 T = 0
3 T = 105 T = 351 T = 100
Q2 ⇒ 6 7 8 9 10 T = 1306 T = 1509 T = 170
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Two phase extraction
1 Normalization• lookup filtering by ’Important triples’ set• normalization is specific for each feature
2 Grouping and aggregating
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Normalization
I Converting clicklog entries to a relational table withthe following attributes:
• feature domain attributes, e.g:• (Q,R,U), (Q,U) for document features• (Q,R), (Q) for query features
• feature attribute valueI Sequential processing ’session-by-session’
• reject spam sessions• emit values (probably repeated)
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Normalization example (I)Click log (with SessionID, TimePassed omitted):Action QueryID RegionID URLsQ 174 0 1625 1627 1623 2510 2524Q 1974 0 2091 17562 1626 1623 1627C 17562C 1627C 1625C 2510
Intermediate table for ’Average click position’ feature:QueryID URLID RegionID ClickPosition1974 17562 0 11974 1627 0 2174 1625 0 1174 2510 0 2
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Normalization example (II)
Click log (sessionID was omitted):Time QueryID RegionID URLs
0 Q 5 0 99 16 87 396 C 84
120 Q 558 0 84 5043 5041 5039125 Q 8768 0 74672 74661 74674 74671145 C 74661
Intermediate table for ’Time to first click’ feature:QueryID RegionID FirstClickTime
5 0 68768 0 20
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Our final ML based solution in a nutshell
I Binary classification task for predicting assessors’ labelsI 26 features extracted from the click logI Gradient Boosted Trees learning model (gbm Rpackage)
I Tuning model’s parameters w.r.t. AUC averaged overgiven query-region pairs
I Ranking URLs according to the best model’sprobability scores
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Data Analysis Scheme1 Given initial training and test sets
2 Partitioning the initial training set into two sets:• training set (3/4)• test set (1/4)
3 Consider the following models:• Gradient Boosted Trees (Bernoulli distribution; 0-1 loss function)• Gradient Boosted Trees (Adaboost distribution; exponential lossfunction)
• Logistic Regression4 Learning and tuning parameters w.r.t. the target metric (Areaunder the ROC curve) on the training set using 3-foldcross-validation
5 Obtain the estimates for the target metric on the test set6 Choose the optimal model, refit it on the whole initial trainingset and apply it on the initial test set
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Data Analysis Scheme1 Given initial training and test sets2 Partitioning the initial training set into two sets:
• training set (3/4)• test set (1/4)
3 Consider the following models:• Gradient Boosted Trees (Bernoulli distribution; 0-1 loss function)• Gradient Boosted Trees (Adaboost distribution; exponential lossfunction)
• Logistic Regression4 Learning and tuning parameters w.r.t. the target metric (Areaunder the ROC curve) on the training set using 3-foldcross-validation
5 Obtain the estimates for the target metric on the test set6 Choose the optimal model, refit it on the whole initial trainingset and apply it on the initial test set
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Data Analysis Scheme1 Given initial training and test sets2 Partitioning the initial training set into two sets:
• training set (3/4)• test set (1/4)
3 Consider the following models:• Gradient Boosted Trees (Bernoulli distribution; 0-1 loss function)• Gradient Boosted Trees (Adaboost distribution; exponential lossfunction)
• Logistic Regression
4 Learning and tuning parameters w.r.t. the target metric (Areaunder the ROC curve) on the training set using 3-foldcross-validation
5 Obtain the estimates for the target metric on the test set6 Choose the optimal model, refit it on the whole initial trainingset and apply it on the initial test set
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Data Analysis Scheme1 Given initial training and test sets2 Partitioning the initial training set into two sets:
• training set (3/4)• test set (1/4)
3 Consider the following models:• Gradient Boosted Trees (Bernoulli distribution; 0-1 loss function)• Gradient Boosted Trees (Adaboost distribution; exponential lossfunction)
• Logistic Regression4 Learning and tuning parameters w.r.t. the target metric (Areaunder the ROC curve) on the training set using 3-foldcross-validation
5 Obtain the estimates for the target metric on the test set6 Choose the optimal model, refit it on the whole initial trainingset and apply it on the initial test set
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Data Analysis Scheme1 Given initial training and test sets2 Partitioning the initial training set into two sets:
• training set (3/4)• test set (1/4)
3 Consider the following models:• Gradient Boosted Trees (Bernoulli distribution; 0-1 loss function)• Gradient Boosted Trees (Adaboost distribution; exponential lossfunction)
• Logistic Regression4 Learning and tuning parameters w.r.t. the target metric (Areaunder the ROC curve) on the training set using 3-foldcross-validation
5 Obtain the estimates for the target metric on the test set
6 Choose the optimal model, refit it on the whole initial trainingset and apply it on the initial test set
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Data Analysis Scheme1 Given initial training and test sets2 Partitioning the initial training set into two sets:
• training set (3/4)• test set (1/4)
3 Consider the following models:• Gradient Boosted Trees (Bernoulli distribution; 0-1 loss function)• Gradient Boosted Trees (Adaboost distribution; exponential lossfunction)
• Logistic Regression4 Learning and tuning parameters w.r.t. the target metric (Areaunder the ROC curve) on the training set using 3-foldcross-validation
5 Obtain the estimates for the target metric on the test set6 Choose the optimal model, refit it on the whole initial trainingset and apply it on the initial test set
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Boosting[Schapire, 1990]
I Given training set (x1, y1), . . . , (xN , yN),yi ∈ {−1,+1}
I For t = 1, . . . , T• construct distribution Dt on {1, . . . , N}• sample examples from it concentrating on the “hardest” ones• learn a “weak classifier” (at least better than random)
ht : X → {−1,+1}
with error εt on Dt:
εt = Pi∼Dt(ht(xi) 6= yi)
I Output the final classifier H as a weighted majorityvote of ht
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AdaBoost[Freund & Schapire, 1997]
I Constructing Dt:• D1(i) =
1N
• given Dt and ht:
Dt+1(i) =Dt(i)
Zt×{e−αt if yi = ht(xi)eαt if yi 6= ht(xi)
where Zt – normalization factor and
αt =1
2ln
(1− εtεt
)> 0
I Final classifier:
H(x) = sign
(∑t
αtht(x)
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Gradient boosted trees[Friedman, 2001]
I Stochastic gradient decent optimization of the lossfunction
I Decision trees model as a weak classifierI Do not require feature normalizationI There is no need to handle missing values specificallyI Reported good performance in relevance predictionproblems [Piwowarski et al., 2009], [Hassan et al.,2010] and [Gulin et al., 2011]
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Gradient boosted treesgbm R package implementation
I There are two available distributions for classificationtasks: Bernoulli and AdaBoost
I Three basic parameters: interaction depth (depth ofeach tree), number of trees (or iterations) andshrinkage (learning rate)
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Logistic regressionglm, stats R package
I Preprocess the initial training data – imputing missingvalues with the help of bagged trees
I Fit the generalized linear model:
f(x) =1
1 + e−z,
where z = β0 + β1x1 + · · ·+ βkxk
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Tuning gbmbernoulli model
3-fold CV estimate of AUC for the optimal parameters: 0.6457435
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Tuning gbmadaboost model
3-fold CV estimate of AUC for the optimal parameters: 0.6455384
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Comparative performance of three optimal modelsTest error estimates
Model Optimal parameter values Test estimate of AUCgbmbernoulli interaction.depth=2, 0.6324717
n.trees=500,shrinkage=0.01
gbmadaboost interaction.depth=4, 0.6313393n.trees=700,shrinkage=0.01
logistic regression - 0.618648
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Contest statistics
I 101 participants, 84 of them are eligible for prizeI Two-stage evaluation procedure: validation set and testset (their sizes were unknown during the contest)
I Validation set size is ≈ 11 000 instancesI Test set size is ≈ 20 000 instances
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Preliminary ResultsValidation set
19th place (AUC=0.650004)
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Final ResultsTest set
34th place (AUC=0.643346)
# Team AUC1 cointegral* 0.6673622 Evlampiy* 0.665063 alsafr* 0.6645274 alexeigor* 0.6631695 keinorhasen 0.6609826 mmp 0.6599147 Cutter* 0.6594528 S-n-D 0.658103. . . . . . . . .34 CLL 0.643346. . . . . . . . .
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Acknowledgements
We would like to thank:
I the organizers from Yandex for an exciting challenge
I E.L. Stolov, V.Y. Mikhailov, V.D. Solovyev and other colleaguesfrom Kazan Federal University for fruitful discussions and support
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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References I
[Freund & Schapire, 1997] Freund, Y., Schapire, R. A decision-theoreticgeneralization of on-line learning and an application to boosting // Journalof Computer and System Sciences. – V. 55. – No. 1. – 1997. – P. 119–139.
[Friedman, 2001] Friedman, J. Greedy Function Approximation: A GradientBoosting Machine // Annals of Statistics. – V. 29. – No.5.– 2001. – P.1189-1232.
[Gulin et al., 2011] Gulin, A., Kuralenok, I., Pavlov, D. Winning TheTransfer Learning Track of Yahoo!’s Learning To Rank Challenge withYetiRank // JMLR: Workshop and Conference Proceedings. – 2011. – P.63-76.
[Hassan et al., 2010] Hassan, A., Jones, R., Klinkner, K.L. Beyond DCG:User behavior as a predictor of a successful search // Proceedings of thethird ACM international conference on Web search and data mining. –ACM. – 2010. – P. 221-230.
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References II
[Piwowarski et al., 2009] Piwowarski, B., Dupret, G., Jones, R. Mining UserWeb Search Activity with Layered Bayesian Networks or How to Capture aClick in its Context // Proceedings of the Second ACM InternationalConference on Web Search and Data Mining. – ACM. – 2009. – P. 162-171.
[Schapire, 1990] Schapire, R. The strength of weak learnability // MachineLearning. – V.5. – No. 2. – 1990. – P. 197–227.
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Outline
1 Problem Statement
2 Features
3 Feature Extraction
4 Statistical analysis
5 Contest Results
6 Appendix A. References
7 Appendix B. R functions
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Compute AUC for gbm modelComputeAUC <- function(fit, ntrees, testSet) {require(ROCR)require(foreach)require(gbm)
pureTestSet <- subset(testSet, select=-c(QueryID, RegionID, URLID, RelevanceLabel))queryRegions <- unique(subset(testSet, select=c(QueryID, RegionID)))count <- nrow(queryRegions)
aucValues <- foreach (i=1:count, .combine="c") %do% {queryId <- queryRegions[i,"QueryID"]regionId <- queryRegions[i,"RegionID"]true.labels <- testSet[testSet$QueryID == queryId & testSet$RegionID == regionId, ]$RelevanceLabelm <- mean(true.labels)if (m == 0 | m == 1) {
pred <- NAperf <- NAcurAUC <- NA
}else {
gbm.predictions <-predict.gbm(fit,pureTestSet[testSet$QueryID == queryId & testSet$RegionID == regionId,],n.trees=ntrees, type="response")
pred <- prediction(gbm.predictions, true.labels)perf <- performance(pred, "auc")curAUC <- perf@y.values [[1]] [1]}
curAUC}return (mean(aucValues, na.rm=T))}
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Tuning AUC for gbm model
TuningGbmFit <- function(trainSet, foldsNum = 3, interactionDepth=4, minNumTrees=100, maxNumTrees = 1500,step=100, shrinkage=.01, distribution="bernoulli", aucfunction=ComputeAUC) {require(gbm)require(foreach)require(caret)require(sqldf)FUN <- match.fun(aucfunction)ntreesSeq <- seq(from=minNumTrees, to=maxNumTrees, by=step)
folds <- createFolds(trainSet$QueryID, foldsNum, T, T)aucvalues <- foreach (i=1:length(folds), .combine="rbind") %do% {
inTrain <- folds[[i]]cvTrainData <- trainSet[inTrain,]cvTestData <- trainSet[-inTrain,]pureCvTrainData <- subset(cvTrainData, select=-c(QueryID, RegionID, URLID))
gbmFit <- gbm(formula=formula(pureCvTrainData), data=pureCvTrainData, distribution=distribution,interaction.depth=interactionDepth, n.trees=maxNumTrees, shrinkage=shrinkage)
foreach(n=ntreesSeq, .combine="rbind") %do% {auc <- FUN(gbmFit, n, cvTestData)(c(n, auc))
}}aucvalues <- as.data.frame(aucvalues)avgAuc <- sqldf("select V1 as ntrees, avg(V2) as AvgAUC from aucvalues group by V1")return (avgAuc)
}
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