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

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

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

Outline Introduction User Study Analysis Discussion

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

User Study Navigation Informational Task Participants Conditions 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 Normal - Google’s search result with no manipulation Swapped -Top two results were switched in order Reversed - 10 search results in reversed order Navigation Find the homepage of Michael Jordan, the statistician. Find the page displaying route map for Greyhound buses. Informational Where is the tallest mountain in New York located? Which actor starred as the main character in the original Time Machine movie?

User Study Data Collection Implicit data Explicit 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%

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?

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

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

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

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?

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

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

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

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