1. To Personalize or Not to Personalize: Modeling Queries with Variation in User Intent Presented by Jaime Teevan, Susan T. Dumais, Daniel J. Liebling Microsoft Research Redmond, WA 98053 USA SIGIR, 2008 2009-02-05 Summarized by Jaeseok Myung Intelligent Database Systems Lab School of Computer Science & Engineering Seoul National University, Seoul, Korea

2. Copyright  2009 by CEBT Introduction  In most previous work on personalized search algorithms The results for all queries are personalized in the same manner  However, there is a lot of variation across queries For some queries, everyone who issues the query is looking for the same thing For other queries, different people want very different results even though they express their need in the same way => Query ambiguity  As found by Dou et al[5], Personalization only improves the results for some queries, and can actually harm other queries Center for E-Business Technology

3. Copyright  2009 by CEBT Introduction (2)  Knowing query ambiguity allows us to Understand users’ intent deeply Personalize when appropriate Center for E-Business Technology microsoft earthstreet maps

4. Copyright  2009 by CEBT Building a Model  How can we build the model? This paper discusses for which data can be used Center for E-Business Technology Model (Classifier) Bayesian Network Logistic Regression SVM … Model (Classifier) Bayesian Network Logistic Regression SVM … Ambiguous Query Unambiguous Query street maps microsoft earth To personalize Not to personalize

5. Copyright  2009 by CEBT Building a Model with Explicit Data  To build a model, let’s consider explicit training data Expensive! Lack of Training Data! Center for E-Business Technology Model Bayesian Network Logistic Regression SVM … Model Bayesian Network Logistic Regression SVM … Explicit Data Train Ambiguous Query Unambiguous Query

6. Copyright  2009 by CEBT Building a Model with Implicit Data  To build a model, let’s consider explicit training data Expensive! Lack of Training Data!  What if we use implicit data to predict query ambiguity? Inexpensive! => robust, reliable Do implicit predict explicit well? – Need to prove Center for E-Business Technology Model Bayesian Network Logistic Regression SVM … Model Bayesian Network Logistic Regression SVM … Implicit Data Train Ambiguous Query Unambiguous Query

7. Copyright  2009 by CEBT Comparing Explicit & Implicit - Methods  If implicit measures are correlated to explicit measures, we can use implicit data instead of explicit data in order to build a predictive model Center for E-Business Technology Explicit Relevance Judgments Explicit Relevance Judgments Large-Scale User Logs (Implicit Features) Large-Scale User Logs (Implicit Features) Measures of Query Ambiguity Using Explicit Data Measures of Query Ambiguity Using Explicit Data Measures of Query Ambiguity Using Implicit Data Measures of Query Ambiguity Using Implicit Data Correlation between Measures Correlation between Measures

8. Copyright  2009 by CEBT Collecting Data Sets  Queries issued to the Live Search from October 4, 2007 to October 11, 2007 For each query, the results displayed to the users and the results that were clicked were extracted from the logs In total, 2,400,645 query instances, covering 44,002 distinct queries By 1,532,022 distinct users  Explicit Relevance Judgments 128 people for 12 of the distinct queries For each query, between 4~81 people judged the top 50 results (presented in random order) as highly relevant, relevant, not relevant In total, 292 sets of judgments were collected Center for E-Business Technology

9. Copyright  2009 by CEBT Query Ambiguity on Explicit Relevance Judgments Center for E-Business Technology Query 12 Highly Relevant RelevantNot Relevant URL10 users 10 users URL25 users3 users2 users ………… URL508 users2 users0 users Query … Highly Relevant RelevantNot Relevant URL10 users 10 users URL25 users3 users2 users ………… URL508 users2 users0 users Query 2 Highly Relevant RelevantNot Relevant URL13 users4 users3 users URL24 users5 users1 users ………… URL504 users 2 users Query 1 Highly Relevant RelevantNot Relevant URL10 users 10 users URL20 users3 users7 users ………… URL508 users2 users0 users Unambiguous Ambiguous

10. Copyright  2009 by CEBT Measures for Explicit Data (1)  Inter-rater Reliability The degree of agreement among raters Gives a score of how much homogeneity, or consensus, there is in the ratings given by judges There are a number of statistics which can be used to determine inter-rater reliability – Joint probability of agreement, Kappa statistics, Correlation coefficients  Fleiss’ Kappa [7] Kappa measures the extent to which the observed probability of agreement (P) exceeds the expected probability of agreement (P e ) if all raters were to make their ratings randomly: Center for E-Business Technology http://en.wikipedia.org/wiki/Fleiss%27_kappa

11. Copyright  2009 by CEBT Measures for Explicit Data (2)  The Potential for Personalization Curve For a group of size one, the best list is one that returns the results that the individual considers relevant first => nDCG = 1 For larger group sizes, a single ranked list can no longer satisfy all individuals perfectly => the average quality drops Center for E-Business Technology User Groups How well a single result can satisfy each group member in a group of that size How well a single result can satisfy each group member in a group of that size A wide gap means big P4P P4P = 1 - nDCG

12. Copyright  2009 by CEBT Comparing Explicit & Implicit - Methods  If implicit measures are correlated to explicit measures, we can use implicit data instead of explicit data in order to build a predictive model Center for E-Business Technology Explicit Relevance Judgments Explicit Relevance Judgments Large-Scale User Logs (Implicit Features) Large-Scale User Logs (Implicit Features) Measures of Query Ambiguity Using Explicit Data Measures of Query Ambiguity Using Explicit Data Measures of Query Ambiguity Using Implicit Data Measures of Query Ambiguity Using Implicit Data Correlation between Measures Correlation between Measures

13. Copyright  2009 by CEBT Measures for Implicit Data (1)  The Implicit Potential for Personalization Curve Constructed using clicks as an approximation for relevance, with clicked results treated as results that were judged relevant It shows that people clicked on the same results for “microsoft earth”, but different results for “street maps”. Center for E-Business Technology

14. Copyright  2009 by CEBT Measures for Implicit Data (2)  Click Entropy Measures the variability in clicked results across individuals A large click entropy means many pages were clicked for the query, while a small click entropy means only a few were Center for E-Business Technology where p(c u |q) is the probability that URL u was clicked following query q microsoft earthstreet maps ? <

15. Copyright  2009 by CEBT Measures of Query Ambiguity To PersonalizeMeasuresNot to Personalize LowKappaHigh P4P(using explicit data)Low HighP4P(using implicit data)Low HighClick entropyLow Center for E-Business Technology A wide gap means big P4P P4P = 1 - nDCG microsoft earthstreet maps <

16. Copyright  2009 by CEBT Comparing Explicit & Implicit Measures  The value of implicit P4P at a group size of four is plotted against the explicit P4P at the same group size Correlation coefficient = 0.77  We can use implicit measures in order to predict query ambiguity Center for E-Business Technology http://en.wikipedia.org/wiki/Correlation_coefficient

17. Copyright  2009 by CEBT Features Used to Predict Ambiguity Center for E-Business Technology Ok... Now we have measures that can be used for predicting the impact of implicit query features. Then, what kinds of implicit features do we have? History NoYes Information Query Query length Contains URL Contains advanced operator Time of day issued Number of results (df) Number of query suggests Reformulation probability # of times query issued # of users who issued query Avg. time of day issued Avg. number of results Avg. number of query suggests Results Query clarity ODP category entropy Number of ODP categories Portion of non-HTML results Portion of results from.com/.edu Number of distinct domains Result entropy Avg. click position Avg. seconds to click Avg. clicks per user Click entropy Potential for personalization

18. Copyright  2009 by CEBT Correlating Features & Implicit Measures Click entropyP4P Query length(words)0.200.17 Query length(chars)-0.04-0.06 URL fragment-0.36-0.33 Location mentioned-0.03-0.02 Advanced query-0.01 # of query suggestions0.120.11 # of times issued0.00-0.02 # of distinct users-0.01-0.02 Avg. # of results0.03 % issued during work-0.10-0.11 Query clarity0.020.03 Category entropy-0.01-0.04 # of distinct categories0.010.03 # of URLs in ODP0.090.12 Top level domain entropy0.020.03 # of distinct hosts0.190.16 Click entropy1.000.87 P4P0.871.00 Result entropy0.530.40 Avg. clicks per users0.730.52 Avg. click position0.900.86 Avg. seconds to click0.030.04 Center for E-Business Technology Small correlation Medium correlation Large correlation No history, No results History, No results No history, Results History, Results

19. Copyright  2009 by CEBT Building a Model  To model query ambiguity, Bayesian dependency networks is employed Center for E-Business Technology URL Word count Very Low Low Medium Yes No =1 2+ Ads High 3+ <3 Model Bayesian Network Logistic Regression SVM … Model Bayesian Network Logistic Regression SVM … Ambiguous Query Unambiguous Query

20. Copyright  2009 by CEBT Prediction Quality  All Features 81% Accuracy  No History, No Results 40% Accuracy  No boost adding result or history 40% Accuracy Center for E-Business Technology History NoYes Information Query Result s

21. Copyright  2009 by CEBT Conclusion  This paper explored using the variation in search result click- through to identify queries that can benefit from personalization  This paper reported that several click-based measures(click entropy and potential for personalization) reliably indicate when different people will find different results relevant to the same query  This paper also examined many additional features of the query, including features of the query string, the result set, and history information about the query Features of the query string alone were able to help us predict variation in clicks Additional information about the result set or query history did not add much value except when taken in conjunction Center for E-Business Technology

22. Copyright  2009 by CEBT Paper Evaluation  Pros Interesting topic Mentioning many practical measures  Cons Lack of explanation about how to build a model Low accuracy Center for E-Business Technology