1. Personalized Query Classification Bin Cao, Qiang Yang, Derek Hao Hu, et al. Computer Science and Engineering Hong Kong UST

2. Query Classification and Online Advertisement

3. QC as Machine Learning Inspired by the KDDCUP’05 competition – Classify a query into a ranked list of categories – Queries are collected from real search engines – Target categories are organized in a tree with each node being a category

4. Our QC Demo http://q2c.cs.ust.hk/q2c/

5. Personalization The aim of Personalized Query Classification is to classify a user query Q to a ranked list of predefined categories for different users QueriesCategories golfCar Sports Places bassEntertainment/Music Living/Fishing Michael Jordan Information/Research Sports/Basketball Shopping

6. PQC: Personalized Query Classification classify a user query Q to a ranked list of categories for different users QueriesCategories golfCar Sports Places bassEntertainment/Music Living/Fishing Michael Jordan Information/Research Sports/Basketball Shopping

7. Question: Can we personalize search without user registration info? Profile based PQC Context based PQC Conclusion

8. Difficulties Web Queries are – Short, sparse: “adi”, ”cs”, “ps” – Noisy: “contnt”, “gogle” – New words are emerging all the time: “windows7” Training data are hard for human to label – Experts may have different understandings for the same ambiguous query E.g. “Apple”, “Office”, etc.

9. Method 1: Profile Based Profile (U) = { } in the past – Profile based Personalized Query Classification -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. Michael Jordan

10. Method 2: Context Based Context = a session of user submitted queries Graphical Model Machine Learning UCB Michael Jordan

11. Outline Introduction Profile based PQC Context based PQC Conclusion

12. How to construct a user profile? To achieve personalized query classification, under independence assumption ACM KDDCUP 2005 Solution: estimating: p(q|c) Focus: estimating p(u|c) for personalization Difficulty: sparseness – Too many possible categories – Limited information for each user p(c|q,u) ∝ p(q|c)p(u|c)p(c)

13. Categorized Clickthrough Data:Too Few! Clickthrough Data Search Engines

14. Collaborative Classification Leverage information from similar users: user- class matrix C1C2C3C4C5 User A√X√?X User B√√?X√ User CXX√?X User D√?√√X √ interested in X not interested in Also can be a value indicate degree of interests

15. Extending Collaborative Filtering (CF) Model to Ranking (Liu and Yang, SIGIR 008) Previous method for CF: – Memory based approach: Finding users having similar interests to help predicting missing values – Model based approach: estimating probability based on new user’s known values We propose a collaborative ranking model to improve model based approach – Using preference or ranking instead of values better at estimating the preference for users

16. Nathan Liu and Qiang Yang. EigenRank: Collaborative Filtering via Rank Aggregation. In ACM SIGIR Conference (ACM SIGIR 08), Singapore, 2008 Predicted Ratings Rating Database Active User Ratings Rating Prediction 1. Item y 2 2. Item y 3 Item List Sort Ranking Collaborative Ranking Framework

17. Collaborative Ranking for Intention Mining Interest Score Matrix P(U|C) Interest Score Matrix P(U|C) |user, or user group| Preference Matrix |Category| |Preference={(URL1<URL2)}| |User| Our objective is to uncover the interest probability P(U|C) consistent with the given observed preference for each query Input Output |Intention category|

18. Solution: Automatically Generate Labeled Data (to assist human labelers) Clickthrough – Connects queries and urls – Contains users’ personal interpretation for query url a url a Query url b url b Query User A User B || C1 C2 We need the category information for urls …

19. Experimental Results: F1 metric

20. How to enlarge training set? 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. A few human labeled data √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. A HUGE number of clickthrough logs without labels Online Knowledge Bases, such as ODP, Wikipedia

21. Online Knowledge Base such as WiKi Knowledge Base Plentiful Documents Links Meaningful Ontology

22. “Label” Retrieval from Online KB Wikipedia Concept Graph Labels on result pages: Shopping: Commercial Sports: non- Commercial Video Games: Commercial Research:non- Commercial Use labeled result pages as “Seeds” to retrieve the most relevant documents as training data Taking Online Commercial Intention as an example

23. Obtain “Pseudo-Relevance” Data 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. 1…. 2…. 3…. A few human labeled data √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. -…. √ …. - …. √ …. - …. √ …. A HUGE number of clickthrough logs We learn a classifier using the retrieved “labeled” documents We apply the classifier to “label” the HUGE clickthrough log We can use the HUGE “label” clickthrough log for evaluation

24. Preliminary results on F(URL)  C We evaluated the performance of the classifier trained with the relevant documents retrieved from Wikipedia AOL query data set, 10,000 held out for test F1 for 18 classes on AOL Query Classification task Number of labeled query Seed Training Queries enriched by search snippets Training documents retrieved from Wikipedia 10012%28%(5,000 Instances) 20021%36%(10,000 Instances) 40031%38%(15,000 Instaces)

25. Outline Introduction Profile based PQC Context based PQC: Hao Hu, Huanhuan Cao, et al. @ SIGIR 2009, ACML 2009. Conclusion

26. Context based PQC for Online Commercial Intention The commercial intention of the same query can be identified given its context information Allan Iverson shoes T-short Michael Jordan Commercial! Offer ads! Commercial! Offer ads!

27. Context based PQC for Online Commercial Intention [Cao etc. SIGIR’09] The commercial intention of the same query can be identified given its context information Graphical Model Machine Learning UCB Michael Jordan Non-Commercial! Redirect to scholar Search! Non-Commercial! Redirect to scholar Search!

28. Two questions: How do we model query context? How do we detect whether two queries are semantically similar? Feature Generation/Enrichment Graphical Models

29. Conditional Random Field Motivation: model the query logs as a conditional random field. Therefore, the relationships between consecutive and even skip queries can be modeled. Question: How do we decide whether two “skip queries” (non-consecutive queries) are related and should be linked? Motivation: model the query logs as a conditional random field. Therefore, the relationships between consecutive and even skip queries can be modeled. Question: How do we decide whether two “skip queries” (non-consecutive queries) are related and should be linked?

30. Semantic Relationship between queries Given Query A and Query B, how do we determine the degrees of relevancy of these two queries in a semantic level? – Send queries to search engines – Obtain search results – Determine distance between search results

31. Context based PQC for Online Commercial Intention The commercial intention of the same query can be identified given its context information Allan Iverson shoes T-short Michael Jordan Commercial! Offer ads! Commercial! Offer ads!

32. Context based PQC for Online Commercial Intention The commercial intention of the same query can be identified given its context information Graphical Model Machine Learning UCB Michael Jordan Non-Commercial! Redirect to scholar Search! Non-Commercial! Redirect to scholar Search!

33. Evaluation Using context information Vs Not using context information

34. Preliminary Experimental Results of PQC for Online Commercial Intention Dataset – AOL Query Log data – Around ~20M Web Queries – Around 650K Web users – Data is sorted by anonymous UserID and sequentially arranged. Each item of clickthrough log data contains – {AnonID, Query, QueryTime, ItemRank, ClickURL}

35. Preliminary Results In our preliminary experimental studies, we annotated four users with the OCI (commercial / non-commercial) status in their clickthrough logs. More larger-scale experimental studies to be followed. Evaluation Metric: Standard F1-measure Baseline classifier: the classifier in Dai’s WWW 2006 work (http://adlab.msn.com/OCI/OCI.aspx) In our preliminary experimental studies, we annotated four users with the OCI (commercial / non-commercial) status in their clickthrough logs. More larger-scale experimental studies to be followed. Evaluation Metric: Standard F1-measure Baseline classifier: the classifier in Dai’s WWW 2006 work (http://adlab.msn.com/OCI/OCI.aspx) F1 for users on AOL Data ModelUser 1User 2User 3User 4 Baseline (non- context) 83.4%82.3%84.0%83.1% Context base PQC 92.7%94.2%91.3%92.6%

36. Preliminary Results The parameter we tune is the threshold we use to determine whether we add the “skip edges” in the CRF model or not.

37. Ongoing work: Personalized Query Classification Efficiency More ground truth data for evaluation

38. PQC and Personalized Search Similar input: – Query Log, Clickthrough Data, IP Address, etc. Different output: – Personalized Search ranked results – PQC Discrete intention categories, Application: advertisements etc.

39. Conclusions: PQC Have user profile information? Profile = Output=Class Method = Collaborative Ranking Have query stream information? Context = Output=Class Method = CRF-based method

40. Q & A