1. Accurately Interpreting Clickthrough Data as Implicit Feedback Joachims, Granka, Pan, Hembrooke, Gay Paper Presentation: Vinay Goel 10/27/05

2. Introduction Adapt a retrieval system to users and or collections Manual adaptation - time consuming or even impractical Explore and evaluate implicit feedback Use clickthrough data in WWW search

3. User Study Record and evaluate user actions Provide insight into the decision process Record users eye movements : Eye tracking

4. Questions used

5. Two Phases of the study Phase I 34 participants Start search with Google query, search for answers Phase II Investigate how users react to manipulations of search results Same instructions as phase I Each subject assigned to one of three experimental conditions Normal, Swapped, Reversed

6. Explicit Relevance Judgments Collected explicit relevance judgments for all queries and results pages Inter-judge agreements

7. Analysis of user behavior Which links do users view and click? Do users scan links from top to bottom? Which links do users evaluate before clicking?

8. Which links do users view and click? Almost equal frequency of 1st and 2nd link, but more clicks on 1st link Once the user has started scrolling, rank appears to become less of an influence

9. Do users scan links from top to bottom? Big gap before viewing 3rd ranked abstract Users scan viewable results thoroughly before scrolling

10. Which links do users evaluate before clicking? Abstracts closer above the clicked link are more likely to be viewed Abstract right below a link is viewed roughly 50% of the time

11. Analysis of Implicit Feedback Does relevance influence user decisions? Are clicks absolute relevance judgments?

12. Does relevance influence user decisions? Yes Use the reversed condition Controllably decreases the quality of the retrieval function and relevance of highly ranked abstracts Users react in two ways View lower ranked links more frequently, scan significantly more abstracts Subjects are much less likely to click on the first link, more likely to click on a lower ranked link

13. Clicks = absolute relevance judgments? Interpretation is problematic Trust Bias Abstract ranked first receives more clicks than the second First link is more relevant (not influenced by order of presentation) or Users prefer the first link due to some level of trust in the search engine (influenced by order of presentation)

14. Trust Bias Hypothesis that users are not influenced by presentation order can be rejected Users have substantial trust in search engines ability to estimate relevance

15. Quality Bias Quality of the ranking influences the users clicking behavior If relevance of retrieved results decreases, users click on abstracts that are on average less relevant Confirmed by the reversed condition

16. Are clicks relative relevance judgments? An accurate interpretation of clicks needs to take into consideration Users trust into quality of search engine Quality of retrieval function itself Difficult to measure explicitly Interpret clicks as pairwise preference statements

17. Strategy 1 Takes trust and quality bias into consideration Substantially and significantly better than random Close in accuracy to inter judge agreement

18. Strategy 2 Slightly more accurate than Strategy 1 Not a significant difference in Phase II

19. Strategy 3 Accuracy worse than Strategy 1 Ranking quality has an effect on the accuracy

20. Strategy 4 No significant differences compared to Strategy 1

21. Strategy 5 Highly accurate in the normal condition Misleading Aligned preferences probably less valuable for learning Better results even if user behaves randomly Less accurate than Strategy 1 in the reversed condition

22. Conclusion Users clicking decisions influenced by search bias and quality bias Strategies for generating relative relevance feedback signals Implicit relevance signals are less consistent with explicit judgments than the explicit judgments among each other Encouraging results