Learning User Behaviors for Advertisements Click Prediction Chieh-Jen Wang & Hsin-Hsi Chen National Taiwan University Taipei, Taiwan

SIGIR 2011 workshop: Internet Advertising Introduction  The commercial value of advertisements on the web depends on whether users click on the advertisements  Predicting potential advertisement clicks of users before target advertisements are displayed is important -advertisement recommendation -advertisement placement -presentation pricing  Problem specification -Given a current search session (q 1, q 2,..., q (i-1) ), we will predict if there is an ad click event when query q i is submitted.

SIGIR 2011 workshop: Internet Advertising Related Work  Advertisiment click prediction model -Feature representation text features (Richardson et al., 2007) demographics features (Cheng & Cantú-Paz, 2010) mouse trajectory features (Guo & Agichtein, 2010) -Machine learning algorithm logistic regression (Richardson, Dominowska, & Ragno, 2007) maximum entropy (Cheng & Cantú-Paz, 2010) support vector machines (Broder et al., 2008) conditional random field (Guo & Agichtein, 2010)

SIGIR 2011 workshop: Internet Advertising Related Work  User search intent -navigational, informational and transactional (Broder, 2002) -noncommercial/commercial & navigational/informational (Ashkan et al., 2009) -research & purchase (Guo & Agichtein, 2010) -receptive & not receptive (Guo & Agichtein, 2010) “receptive” (i.e., an advertisement click is expected in a future search within the current session) “not receptive” (i.e., not any future advertisement clicks are expected within the current session)

SIGIR 2011 workshop: Internet Advertising Overview

SIGIR 2011 workshop: Internet Advertising Overview

SIGIR 2011 workshop: Internet Advertising Microsoft AdCenter Logs  Time: ~ (84 days)  The Microsoft AdCenter logs include: -101 million impressions million clicks million sessions (5.06 million sessions contain at least one click)  An impression is defined as a single search results page described by a set of attributes  A session is defined by a repeated search engine usage of intervals of 10 minutes and less, with a total session not longer then 8 hours

SIGIR 2011 workshop: Internet Advertising Data Purify  For the purposes of promotions, some specific queries are issued or advertisements are clicked by software robots  Filter criteria -issue queries more than 7 times in any 10 second interval -issue queries at two distinct places at the same time -click an advertisement more than one time in any 5 second interval -duplicated impression IDs  Data partition -Training: sessions which contain at least one advertisement click in the first 56 days -Testing: sessions in the last 28 days

SIGIR 2011 workshop: Internet Advertising Experiment Datasets TrainingTesting # of sessions (clicks)3.12M1.42M # of sessions (non-clicks)010.61M # of click impressions3.75M1.73M # of non-click impressions6.92M37.41M

SIGIR 2011 workshop: Internet Advertising Overview

SIGIR 2011 workshop: Internet Advertising Feature Extraction  Feature representation -Every impression q i (1  i  n) in session s = (q 1, q 2,..., q (i-1), q i, q (i+1),..., q n ) is represented as a feature vector -q i itself (Current Impression Level) -the first impression q 1 (First Impression Level) -the previous n impression q (i-n) (Previous n Impression Level) -all the contextual impressions q 1, q 2,..., q (i-1) in s (Contextual Impression Level)  Labeling -click if impression q i contains at least one advertisement click, otherwise non- click.

SIGIR 2011 workshop: Internet Advertising Feature Extraction from Current Impression Level  These features aim to capture query information, users’ intent and the similarity between current query an previous one  QC (query category) -14 categories (exclusive of “Regional” and “World”) on the 2nd level of the Open Directory Project (ODP) ontology to represent query categories  QIntent (query intent) -4,020 intent clusters are learned from MSN Search Query Log excerpt (Wang et al., 2010) -QIntent is specified by the distribution of the top 100 similar intent clusters FeatureDescriptionFeatureDescription QP Position of q i in s, i.e., iQtypeType of query in q i : information, navigation, or transaction #QT Number of query terms in q i QCODP categories of query in q i QT Query terms in q i QIntentIntent type of query in q i IsURLQ1 if the query in q i is in the form of a URL, and 0 otherwise QSim Cosine similarity between query terms in q i and q i-1 QDMADMA level user location ID of q i QOverlapOverlapping between query terms in q i and q i-1

SIGIR 2011 workshop: Internet Advertising Feature Extraction from First Impression Level  These features aim to capture an initial search goal of a session. FeatureDescriptionFeatureDescription FQQuery terms in q 1 TimeToFQTime duration (in seconds) between q 1 and q i

SIGIR 2011 workshop: Internet Advertising Feature Extraction from Previous n Impression Level  These features aim to capture the advertisements clicks information of the previous n impression.  In our experiments, n is set to 1 and 2 FeatureDescriptionFeatureDescription PNP n Page number of the result page of q (i-n) ClickDNP n URLdomain names of clicked advertisements in the result page of q (i-n) #AdP n Number of advertisements displayed in the result page of q (i-n) AdCP n ODP categories of the clicked advertisements in q (i-n) IsClickP n 1 if there is at least one advertisement click in q (i-n), and 0 otherwise AdIntentP n Intent types of the clicked advertisements in q (i-n) T#ClickP n Total number of clicked advertisements in q (i-n) TimeToP n Time duration (in seconds) between q (i-n) and q i ClickRP n The ranks of clicked advertisements in the result page of q (i-n) #AdoverlapDisplayed advertisements overlapping between q i-n and q i-(n+1)

SIGIR 2011 workshop: Internet Advertising Feature Extraction from Contextual Impression Level FeatureDescriptionFeatureDescription T#AdTotal advertisements reported in q 1, q 2,..., q (i-1) ConClicki-j where qj, q(j+1),..., q(i-1) contain clicked advertisements continuously T#ClickTotal number of clicked advertisements in q 1, q 2,..., q (i-1) NearClicki-j where qj is the nearest impression containing clicked advertisements CTRAdvertisements click through ratio before qi = total clicked ads divided by total ads before qi CTQCODP categories of queries in q1, q2,..., q(i-1) number of advertisement reports at rank m of q1, q2,..., q(i-1), where m=1, 2,..., 8 CTQIntentIntent types of queries in q1, q2,..., q(i-1) m Total number of advertisements clicks at each rank of q1, q2,..., q(i-1) CTAdCODP categories of clicked advertisements in q1, q2,..., q(i-1) through ratio for each rank at q1, q2,..., q(i-1) CTAdIntentIntent types of clicked advertisements in q1, q2,..., q(i-1) T#ConCli ck Total number of advertisements clicked in q 1, q 2,..., q (i-1) CTIntentDisIntents of clicked advertisements in q1, q2,..., q(i-1) after disambiguation

SIGIR 2011 workshop: Internet Advertising Feature Extraction from Contextual Impression Level  These features represent a sequence of users’ behaviors  Weight of intent types of submitted queries (CTQIntent) and clicked advertisements (CTAdIntent) in the access history is defined as: -P m is a probability of the type m intent -w j denotes a query or a clicked advertisement in q j  Weight of ODP categories (CTQC & CTAdC) Jelinek-mercer smoothing

SIGIR 2011 workshop: Internet Advertising Overview

SIGIR 2011 workshop: Internet Advertising Click Prediction Model  Four learning algorithms -Conditional Random Fields (CRF) -Support Vector Machine (SVM) kernel function (RBF, linear kernel) parameter optimization (grid algorithm for c and g) -Decision Tree C4.5 Tree -Back-Propagation Neural Networks Hidden Layer =2 Learning rate = 0.8 Momentum = 0.2

SIGIR 2011 workshop: Internet Advertising Feature Selection Algorithm  Random Subspace Method (RS) -an ensemble classifier that consists of several classifiers -prediction is through a majority vote from the classifiers  F-Score (FS) & Information Gain (IG) -greedy inclusion algorithm -retain a number of the best terms or features for use by the classier

SIGIR 2011 workshop: Internet Advertising Overview

SIGIR 2011 workshop: Internet Advertising Performance of Advertisements Click Prediction All FeaturesNon-click typeClick type ModelAccPrecRecF1F1PrecRecF1F1 Guess MM CRF DT BPN SVM (RBF) SVM (Linear)  Metrics -accuracy (Acc), precision (Prec), recall (Rec), and F-measure (F1)  Baseline -guessing the majority class (non-click) is one baseline. -Markov Model (MM), formulated by query transition.

SIGIR 2011 workshop: Internet Advertising Performance of Feature Selection Features SelectionNon-click typeClick type ModelAccPrecRecF1F1PrecRecF1F1 CRF(ALL) CRF(RS15) CRF(RS25) CRF(RS35) CRF(RS45) CRF(FS) CRF(IG) SVM(ALL) SVM(RS15) SVM(RS25) SVM(RS35) SVM(RS45) SVM(FS) SVM(IG)

SIGIR 2011 workshop: Internet Advertising Top-10 Important Features F-ScoreInformation Gain RankFeatureFLRIFeatureFLRI 1QTCI1QTCI1 2CTAdIntentCT CTIntent Dis CT CTIntent Dis CT0.6498CTQIntentCT CTQIntentCT0.5092T#ClickP 1 PI FQFI0.3557CTRCT IsClickP 1 PI0.3222T#AdCT CTRCT0.3052ConClickCT T#ClickP 1 PI0.2943CTAdIntentCT ConClickCT0.2688NearClickCT NearClickCT0.2568QtypeCI0.2082

SIGIR 2011 workshop: Internet Advertising Conclusion and Future Work  We explore the effects of various intent-related features on advertisements click prediction  CRF model performs better than two baselines and SVM significantly  When random subspace method is introduced to feature selection, the precision of click prediction is increased from to  In the future, we plan to expand our model to consider fine-grained user intent and user interactions  In addition, we will extend this approach to predict which advertisements will be clicked

SIGIR 2011 workshop: Internet Advertising Thank You Q & A