1. Developing and Evaluating a Query Recommendation Feature to Assist Users with Online Information Seeking & Retrieval With graduate students: Karl Gyllstrom, Earl Bailey Diane Kelly, Assistant Professor University of North Carolina at Chapel Hill

2. Background  Query formulation is one of the most important and difficult aspects of information seeking  Users often need to enter multiple queries to investigate different aspects of their information needs  Some techniques have been developed to assist users with query formulation and reformulation:  Term Suggestion  Query Recommendation  However, there are problems associated with each of these techniques … ALISE Conference | January 23, 2009 | Denver, CO

3. Problems  Term Suggestion  Works via relevance feedback (often times ‘pseudo’ relevance feedback is used which makes assumptions about the goodness of the initial query)  Users don’t have the additional cognitive resources to engage in explicit feedback (‘form’ is awkward)  Users are too lazy to provide feedback – principle of least effort (‘form’ is cumbersome)  Terms are not presented in context so it may be hard for users to understand how they can help ALISE Conference | January 23, 2009 | Denver, CO

4. Problems  Query Suggestion  It is hard to determine the similarity of previous queries to one another (and to the current query)  Sparsity problem: assumes a set of queries that are similar to the current query exists ALISE Conference | January 23, 2009 | Denver, CO

5. Our Approach  User Query: dog law enforcement SUGGESTED TERMS Canine Legal Charge Train Drug Traffic Police Search Officer Dog law enforcement canine Canine legal drug traffic Dog law police enforcement drug Dog law police drug search SUGGESTED QUERIES ALISE Conference | January 23, 2009 | Denver, CO

6. Studies  Study I (System/Algorithm Evaluation no Users)  Identify and evaluate techniques for identify terms from corpus given a query  Identify and evaluate techniques for using these terms to create effective and semantically meaningful queries  Studies II-IV (Interactive Evaluation with Users)  Evaluate automatic query suggestion techniques, including  Comparison with term suggestions  Comparison with user-generated suggestions  Investigation of effects of topic difficulty and familiarity  Compare ‘remote’ study mode with laboratory study mode ALISE Conference | January 23, 2009 | Denver, CO

7. Study I: Some Questions  How do we identify the best terms from the corpus given the user’s query?  How do we select the best terms from those generated?  In what order do we combine terms?  How do we incorporate the initial query?  How long should the recommended queries be?  How many queries do we suggest?  Our Solution  Implemented Tan, et al.’s (2007) clustering method for selecting terms (language modeling framework)  TREC-style evaluation using a test collection ALISE Conference | January 23, 2009 | Denver, CO

8. Studies II-IV: Common Elements  Two interfaces: Query Suggestion and Term SuggestionQuery Suggestion  Each subject completed two search topics with each interface  Task: Find and save documents relevant to the information described in the topic  Up to 15 minutes to search per topic  Twenty search topics in total sorted into four difficulty levels : Easy, Medium, Moderate, Difficult  Each subject completed one topic from each level  Rotation and counter-balancing …  Subjects searched a closed corpus of over 1 million newspaper articles (AP, NYT and XN) ALISE Conference | January 23, 2009 | Denver, CO

9. Studies II-IV: Common Elements  Several outcome measures:  Use of suggestions (System Log)  Performance (Retrieval Results and Docs Saved)  Mean Average Precision (Baseline Relevance Assessments)  Interactive Precision and Recall (Integrate BRA with User RA)  Discounted Cumulated Gain (User RA)  Perceived Effectiveness and Satisfaction (Exit Questionnaire)  Preference (Exit Questionnaire)  Qualitative Feedback (Exit Questionnaire) ALISE Conference | January 23, 2009 | Denver, CO

10. Studies II-IV: Common Elements  And a few more independent variables:  Topic Difficulty (Pre-determined Level)  Subject’s Topic Knowledge (Pre-topic Questionnaire)  Subject’s Experienced Difficulty (Exit Questionnaire) ALISE Conference | January 23, 2009 | Denver, CO

11. Studies II-IV: Common Procedures START END Pre-Topic Questionnaire [Repeat for 2 Systems] Exit Questionnaire Consent Subject Searches [Repeat for 2 Topics] Demographic Questionnaire Search Experience Questionnaire ALISE Conference | January 23, 2009 | Denver, CO

12. Studies II-IV: Differences  Study II (n=43)  Subjects completed this study remotely  Study III (n=25)  Eye-tracking data collected from first 12 subjects  Study IV (n=22)  Additional qualitative data collection via stimulated recall for two searches (one per system)  Study III and IV  Variation in Source of Suggestions: Half received system- generated suggestions (same as Study II) and half received user- generated suggestions (extracted from Study II subjects) ALISE Conference | January 23, 2009 | Denver, CO

13. Preliminary Results  Use ALISE Conference | January 23, 2009 | Denver, CO

14. Preliminary Results  Use and Source of Suggestions ALISE Conference | January 23, 2009 | Denver, CO

15. Preliminary Results  Use & Topic ALISE Conference | January 23, 2009 | Denver, CO

16. Preliminary Results  Perceived Effectiveness and Satisfaction  For 7 of the 11 Exit Questionnaire items, query suggestion was rated higher than term suggestion. These items concerned:  ‘Cognitive Assistance’ (e.g., helped me think more about the topic and understand its different aspects)  Satisfaction  Term suggestion was rated higher with respect to  Modification  Ease of Use  There were few differences in ratings of system-generated suggestions and user-generated suggestions ALISE Conference | January 23, 2009 | Denver, CO

17. Preliminary Results ALISE Conference | January 23, 2009 | Denver, CO  Preference

18. Next Steps  Continue data analysis …  Impact of topic difficulty and knowledge  Eye-tracking data  ‘Typing’ of suggestions  Temporal/Stage Analysis ALISE Conference | January 23, 2009 | Denver, CO

19. BACK ALISE Conference | January 23, 2009 | Denver, CO