Accurately Interpreting Clickthrough Data as Implicit Feedback

Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, & Geri GayCornell UniversitySIGIR 2005

Presented by Rosta FarzanPAWS Group Meeting

Problem

Adapting retrieval systems requires large amount of data

Explicit DataImplicit Data

ExpensiveNoisy 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 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

NavigationFind the homepage of Michael Jordan, the statistician.Find the page displaying route map for Greyhound buses.

InformationalWhere 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

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