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Accurately Interpreting Clickthrough Data as Implicit Feedback

This presentation by Thorsten Joachims et al. evaluates the efficacy of implicit feedback derived from clickstream data in assessing user behavior during online searches. Focusing on user studies involving eye-tracking and logging, the research explores how users interact with search results and which factors influence their clicking decisions. The findings reveal key insights into user engagement patterns, including the significance of document relevance and the effects of manipulated search result rankings. The goal is to better understand how to adapt retrieval systems to leverage implicit user feedback for improved performance.

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Accurately Interpreting Clickthrough Data as Implicit Feedback

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  1. Accurately Interpreting Clickthrough Data as Implicit Feedback Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, & Geri Gay Cornell University SIGIR 2005 Presented by Rosta Farzan PAWS Group Meeting

  2. Problem Adapting retrieval systems requires large amount of data Implicit Data Explicit Data Expensive Noisy and unreliable

  3. Goal Evaluate which types of implicit feedback can reliably be extracted from observed users behavior

  4. Outline • Introduction • User Study • Analysis • Discussion

  5. Introduction • Designing a study to evaluate the reliability of implicit feedback • How users interact with the list of ranked results from Google search • Two types of analysis • Analysis of users’ behavior • Using eye-tracking & logging • Do users scan from top to bottom? • How many abstracts do they read before clicking? • How does users’ behavior change if the result are manipulated artificially? • Analysis of Implicit Feedback • Comparing implicit feedback with explicit feedback collected manually

  6. User Study • Task • Five navigational • Find related web pages • Five informational • Find specific information • Users read each question in turn and answered orally when they found the answer • Participants • Phase I • 34 undergraduate, different major • Used data from 29 because of eye-tracking issues • Phase II • 22 participants, 16 were used • Conditions • Phase I • Normal - Google’s search result with no manipulation • Phase II • Normal - Google’s search result with no manipulation • Swapped -Top two results were switched in order • Reversed - 10 search results in reversed order

  7. User Study • Data Collection • Implicit data • HTTP-proxy server logs all click-stream data • Eye-tracking • fixations • Explicit data • Five judges for each two questions plus 10 results pages from two other questions • Order the randomized results by how relevant they are • Relative decision making • Inter-judges agreement • Phase I (ordering top 10): 89.5 % • Phase II (ordering all results): 82.5%

  8. 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?

  9. Which Links do Users View and Click? User click substantially more often on the first than second link Scrolling

  10. Do Users Scan Links from Top to Bottom? On average users tend to read from top to bottom There is a big gap before viewing the third-ranked Users first scan the viewable results quite thoroughly before scrolling

  11. Which Links do Users Evaluate before Clicking? They view substantially more abstracts above than below the click

  12. Analysis of Implicit Feedback • How relevance of the document to the query influence clicking decision? • What Clicks tell us about the relevance of a document?

  13. Does Relevance Influence User Decision? • Using “reversed” condition • Lower quality of retrieval • Users react to the relevance of the presented links • Users view lower ranked links more frequently • Scan significantly more abstracts • Users clicked less on first rank • Users clicked more often on low ranked

  14. Are Clicks Absolute Relevance Judgments? • Trust bias • Ranked first receives many more clicks • Quality bias • Comparing clicking behavior in “normal” condition vs. “reversed” condition. • On lower quality, users click on abstracts that are on average less relevant

  15. Are Clicks Relative Relevance Judgments? • Consider not-clicked links as well as clicks as feedback signals • Example: l1 l2 l3 l4 l5 l6 l7 • Strategy 1 – Click > Skip Above • Rel(l3) > rel(l2), rel(l5) > rel(l2), rel(l5) > rel(l4) • Phase I data supports this strategy but phase II doesn’t • Strategy 2 – Last Click > Skip Above • Earlier clicks might be less informed than later clicks • Rel(l5) > rel(l2), rel(l5) > rel(l4) • Still not supported by phase II data

  16. Strategies • Strategy 3 – Click > Earlier Click • Click later in time are on more relevant abstracts • Assuming order of clicks as 3, 1, 5 • Rel(l1)>rel(l3), rel(l5)>rel(l3), rel(l5)>rel(l1) • Not supported by data • Strategy 4 – Last Click > Skip Previous • Constraint only between a clicked link and a not-clicked link immediately above • Result is similar to strategy 1 • Strategy 5 – Click > No-Click Next • Constraint between a clicked link and an immediately following link

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