The Use of Query Reformulation to Predict Future User Actions
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The Use of Query Reformulation to Predict Future User Actions

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The Use of Query Reformulation to Predict Future User Actions Presentation Transcript

  • 1. Using Query Reformulation for User Profiling Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address] Interested in how much descriptive information we can generate about a people by leveraging search log data .
  • 2. What Did We Find Out?
    • We can tell quite a lot about a user!
    • When combined with other information, query reformulation is a revealing searching characteristic.
  • 3. The State of Web Search
    • Why search data is important
  • 4. The Power of Search and the Web
    • Search is the top online activity
    • Search drives over 7 billion monthly queries in the U.S.
    • Online activity has a huge impact on people’s daily lives:
      • 70 minutes less with family
      • 30 minutes less TV
      • 8.5 minutes less sleep
    Sources: comScore, U.S., Feb. ’06, Stanford Institute for the Quantitative Study of Society, Nov. ‘05
  • 5. Analysis of Search Marketplace Holding fairly stable over the last year or so, albeit with some Bing flux
  • 6. Search Logs
    • Contains the trace data recorded when a person visits the search engine, submits a query, views results, etc
    • On one hand, logs have been criticized for not being rich enough (i.e., only have behaviors but not the ‘why ’ factors)
    • On the other hand, logs have been criticized for recording too much about us (i.e., logging a lot of personal information about a person)
    search logs How much we can learn about a person from the data stored in search logs? Specifically, how rich of a searcher profile can we build of what a person is doing, of why they are doing it, and to predict what are they going to do next?
  • 7. An illustrative example
  • 8. How much can we tell from a single query?
    • ASIS&T is an acronym for the American Society of Information Science and Technology
    • Good probability that this user is an academic , a researcher, a librarian, or a student in one of these disciplines
    • Leveraging demographic information :
      • 57 percent female / 43 percent male probability
      • 66.2 percent chance works in the information science field
      • 55.6 percent probability this user has master’s degree
  • 9. How much can we tell from a single query?
    • Leveraging demographic information (cont’d):
      • 32.3 percent probability this user has a doctorate
      • 53 percent likelihood works in academia.
    • Using IP , we can locate the geographical area
    • Based on time , could infer that:
      • this person is searching for the conference’s schedule (if the query is submitted prior to the meeting) for travel
      • or looking for presentations or papers from the meeting (if the query is submitted after the conference).
    Theoretically, we can tell a lot ! However, with billions of queries per month, we can’t do the analysis by hand like this example. To develop user profiles, we need automated methods . Research Question - How complete of a profile can one develop for a Web search engine user from search log data? [(a) what the user is doing, (b) what the user is interested in, and (c) what the user intends to do]
  • 10. Specific aspects with automated methods …
    • Location
    • Geographical interest
    • Topical interest
    • Topical complexity
    • Content desires
    • Commercial intent
    • Purchase intent
    • Potential to click on a link
    • Gender
    • User identification
    • – where the user is at
    • – where the user is going
    • – what the user is interested in
    • – how motivated is the user
    • – Info, Nav, Transactional
    • – eCommerce related
    • – getting ready to buy
    • – will user click on link
    • - demographic targeting/personalization
    • - specific user targeting
  • 11. Automated methods using query reformulation
    • Location
    • Geographical interest
    • Topical interest
    • Topical complexity – n-grams pattern analysis
    • Content desires
    • Commercial intent
    • Purchase intent
    • Potential to click on a link
    • Gender
    • User identification
  • 12. Where to get full story?
    • The methodological implementation reported in paper in your ASIST proceedings:
    • Jansen, B.J., Zhang, M., Booth, B. Park, D., Zhang, Y., Kathuria, A. and Bonner, P. (2009) To What Degree Can Log Data Profile a Web Searcher? Proceedings of the American Society for Information Science and Technology 2009 Annual Meeting. Vancouver, British Columbia. 6-11 November.
  • 13. Topical Complexity
    • Number of queries by a user in a session on a topic can tell us many things:
      • the complexity of the topic
      • the user’s motivation for the need
      • provide prediction of future action
  • 14. Information Searching
    • Probabilistic user modeling
      • increasingly important area
      • allows computer systems to adapt to users
    • Algorithmic techniques typically employ state models
      • Simple Bayesian Classifier
      • Markov Modeling
      • n-grams
    Note: not always ‘informational’ anymore. Many time people are searching for ‘ other things ’. Rose & Levinson (2004); Jansen, Booth, & Spink (2008).
  • 15. Illustration of Probabilistic User Modeling Using n-grams Given these states … … how accurately can we predict these? AC 5 A 4 ABCDE 3 ABCDE 2 ABCF 1 Search State Transitions User 40% D C 60% B A 100% E CD 66% D BC 1OO% C AB Accuracy Next State? Predictive Pattern
  • 16. Example Using Search Log
    • ~ 965,000 searching sessions
    • ~ 1,500,000 queries
    • 8 states focusing on query reformulation
    • Similar results for other aspects of searching
    • See - Qui (1993), Jansen (2005), Jansen & McNeese (2006)
    • Maybe ‘states’ are not the correct paradigm?
    Jansen, B. J., Booth, D. L., & Spink, A. (2009). Patterns of query modification during Web searching . Journal of the American Society for Information Science and Technology . 10% improvement from 1 st to 2 nd order: okay, but would like to do better 0 1 st 2 nd 3 rd 4 th Order of the Model Accuracy of Prediction 0.1 0.2 0.3 0.4 0.5 0.6 0.28 0.40 0.47 0.44 0.44 0.60 Drop out rate (folks who don’t submit a query ~40%)
  • 17. User Profiling Framework
    • Classify user aspects into two levels: internal and external .
    • Internal aspects refer to attributes of the users themselves.
    • External aspects relate to the behavior or interest of the users.
    • Interaction between internal and external aspects. Can infer external aspects from internal aspects. External aspects reflect internal aspects
  • 18. Thank you! (open for questions and further discussion) Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]
  • 19. Search Logs has some common fields, such as time, queries, results, etc. We can enrich the log with additional fields. Back Back
  • 20. Back
  • 21. Back