Consistent Phrase Relevance Measures

Scott Wen-tau Yih & Chris MeekMicrosoft Research

Why Measure Phase Relevance?

Keyword-driven Online AdvertisingSponsored Search

Ads with bid keywords that match the queryContextual Advertising (keyword-based)

Ads with bid keywords that are relevant to the content

To deliver relevant ads leads to problems related to phrase relevance measures.

Sponsored Searchqueryflight to kyoto

Are these ads relevant to the query?

Contextual Advertising

How relevant are the keywords behind the ads?

Problem – Phrase Relevance MeasuresGiven a document d and a phrase ph, we want

to measure whether ph is relevant to d (e.g., p(ph|d))

Applications – judging ad relevanceSponsored search (query vs. ad landing page)

Ad relevance verificationWhether a keyword/query is relevant to the page

Contextual advertising (page vs. bid keyword)External keyword verificationWhether the new keyword is relevant to the content page

Keyword Extraction for In-doc PhrasesFor in-document phrases, we can use keyword

extractor (KEX) directly [Yih et al. WWW-06]

Machine Learning model learned by logistic regressionUse more than 10 categories of features

e.g., position, format, hyperlink, etc.Digital Camera ReviewThe new flagship of Canon’s S-series, PowerShot S80 digital camera, incorporates 8 megapixels for shooting still images and a movie mode that records an impressive 1024 x 768 pixels.

KEX

truecredit 0.879

transunion 0.705

credit bureaus 0.637

id theft 0.138

…

TrueCreditGet immediate access to your complete credit report from 3 credit bureaus. Just $14.95 per month, including $25K ID Theft insurance. Contact TransUnion for more detail…

What if the phrase is NOT in the document?

Challenges of Handling Out-of-doc PhrasesGiven a document d and a phrase ph that is not

in dEstimate the probability that ph is relevant to d

truecredit 0.879

transunion 0.705

credit bureaus 0.637

id theft 0.138

…

TrueCreditGet immediate access to your complete credit report from 3 credit bureaus. Just $14.95 per month, including $25K ID Theft insurance. Contact TransUnion for more detail…

credit bureau report ?

credit report services ?

equifax credit bureau ?

equifax credit report ?

exquifax ?

equfax ?

trans union canada ?

…

Challenges of Handling Out-of-doc PhrasesGiven a document d and a phrase ph that is not

in dEstimate the probability that ph is relevant to d

ChallengesHow do we measure it?

Lack of contextual information that in-doc phrases have

Consistent with the probabilities of in-doc phrasesMay need some methods to calibrate probabilities

Two ApproachesCalibrated cosine similarity methods

Treat in-doc and out-of-doc phrases equallyMap cosine similarity scores to probabilities

Regression methods based on semantic kernelsGiven robust in-doc phrase relevance measuresPredict out-of-doc phrase relevance using similarity between the target phrase and in-doc phrases

Regression methods achieve better empirical results

Outline

IntroductionRelevance measures using cosine similarityOut-of-doc phrase relevance measure using Gaussian process regressionExperimentsConclusions

Similarity-based MeasuresStep 1: Estimate sim(d,ph) → R

Represent d as a sparse word vectorWords in document d, associated with weightsVec(d) = {‘truecredit’,0.9; ‘transunion’,0.7; ‘access’,0.1; … }

Represent ph as a sparse word vector via query expansion

Issue ph as a query to search engine; let the result page be document d’Vec(ph) ← Vec(d’)

sim(d,ph) = cosine(Vec(d),Vec(ph))

Choices of term-weighing schemesBag of words (SimBin), TFIDF (SimTFIDF)Keyword Extraction (SimKEX)

Map Similarity Scores to ProbabilitiesStep 2: Map sim(d,ph) to prob(ph|d)

Via a sigmoid function where the weights are pre-learned[Platt ’00]

The sigmoid function can be used to combine multiple relevance scores

SimCombine: Combine SimBin, SimTFIDF & SimKEX

),(1

),(log

phdsim

phdsimf

)exp(1

1)|(

fdphprob

)exp(1

1)|(

m

i ii fdphprob

Outline

IntroductionRelevance Measures using cosine similarityOut-of-doc phrase relevance measure using Gaussian process regressionExperimentsConclusions

Regression-based Measures: Intuition Relevant in-doc

phrases:TrueCredit, TransUnion

Out-of-doc phrases:credit bureau report vs. Olympics

Which out-of-doc phrase is more relevant?

TrueCreditGet immediate access to your complete credit report from 3 credit bureaus. Just $14.95 per month, including $25K ID Theft insurance. Contact TransUnion…

TrueCreditGet immediate access to your complete credit report from 3 credit bureaus. Just $14.95 per month, including $25K ID Theft insurance. Contact TransUnion…

Regression-based Measures: ProcedureStep 1: Estimate probabilities of in-doc phrases

KEX(d) = {(‘truecredit’,0.88),(‘transunion’,0.71), (‘credit bureaus’,0.64), (‘id theft’,0.14)}

Step 2: Represent each phrase as a TFIDF vector via query expansionx1=Vec(‘truecredit’), y1=0.88; x2=Vec(‘transunion’), y2=0.71x3=Vec(‘credit bureaus’), y3=0.64; x4=Vec(‘id theft’), y4=0.14

Step 3: Represent the target phrase ph as a vectorx =Vec(ph), y=?

Step 4: Use a regression model to predict yInput: (x1, y1), …, (xn, yn) and x

Output: y

Gaussian Process Regression (GPR)We don’t specify the functional form of the regression modelInstead, we only need to specify the “kernel function”

k(x1, x2): linear kernel, polynomial kernel, RBF kernel, etc.

Conceptually, kernel function tells how similar x1 & x2 areChanging kernel function changes the regression function

Linear kernel → Bayesian linear regression

GPR

(x1,y1), (x2,y2),…, (xn,yn)

xkernel function e.g., k(xi,xj) = xi·xj

yyIKk 1Τ )( 2

ny

O(N3) from matrix inversion, where N≤20 typically

Outline

IntroductionRelevance Measures using cosine similarityOut-of-doc phrase relevance measure using Gaussian process regressionExperimentsConclusions

DataFrom sponsored search ad-click logs (3-month period in 2007)

Randomly select 867 English ad landing pagesEach page is associated with the original query and ~10 related keywords (from internal query suggestion algorithms)

Labeled 9,319 document-keyword pairs4,381 (47%) relevant; 4,938 (53%) irrelevantMost keywords (81.9%) are out-of-document

10-fold cross-validation when learning is used

Evaluation MetricsAccuracy

Quality of binary classificationFalse positive and false negative are treated equally

AUC (Area Under the ROC curve)Quality of rankingEquivalent to pair-wise accuracy

Cross EntropyQuality of probability estimations

-log2[p(ph|d)] if ph is labeled relevant to d

-log2[1-p(ph|d)] if ph is labeled irrelevant to d

Accuracy

1

2

3

4

5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.651000000000001

0.663000000000001

0.654000000000001

0.681000000000001

0.704000000000001

Better

AUC Scores

1

2

3

4

5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.702000000000001

0.726000000000001

0.726000000000001

0.752000000000001

0.773000000000001

Better

Cross Entropy

1

2

3

4

5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.939

0.887

0.882

0.864000000000001

0.835000000000001

Better

Conclusions (1/2)Phrase relevance measure is a crucial task for online advertisingOur solution: similarity & regression based methods

Consistent probabilities for out-of-doc phrasesSimilarity-based methods

Simple and straightforwardThe combined approach can lead to decent performance

Regression-based methodsAchieved the best results in our experimentsQuality depends on the in-doc relevance estimates & kernel

Conclusions (2/2)Future Work – More machine learning techniques

SimCombineAn ML method using basic similarity measures as featuresExplore more features (e.g., query frequency, page quality)Other machine learning models

Gaussian process regressionLearning a better kernel function

Kernel meta-training [Platt et al. NIPS-14] Maximum likelihood training