NDLI: Understanding how people interact with web search results that change in real-time using implicit feedback

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Understanding how people interact with web search results that change in real-time using implicit feedback

Content Provider	ACM Digital Library
Author	Kim, Jin Young Teevan, Jaime Lagun, Dmitry Cramer, Mark
Abstract	The way a searcher interacts with query results can reveal a lot about what is being sought. Considerable research has gone into using implicit relevance feedback to identify relevant con-tent in real-time, but little is known about how to best present this newly identified relevant content to users. In this paper we compare a traditional search interface with one that dynamical-ly re-ranks and recommends search results as the user interacts with it in order to build a picture of how and when users should be offered dynamically identified relevant content. We present several studies that compare logged behavior for hun-dreds of thousands of users and millions of queries as well as self-reported measures of success across the two interaction models. Compared to traditional web search, users presented with dynamically ranked results exhibit higher engagement and find information faster, particularly during exploratory tasks. These findings have implications for how search engines might best exploit implicit feedback in real-time in order to help users identify the most relevant results as quickly as possible.
Starting Page	2321
Ending Page	2326
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505663
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Implicit relevance feedback Query log analysis Dynamic ranked retrieval Web search Interactive information retrieval
Content Type	Text
Resource Type	Article

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