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  1. Transactions on the Web (TWEB)
  2. ACM Transactions on the Web (TWEB) : Volume 6
  3. Issue 1, March 2012
  4. Quality and Leniency in Online Collaborative Rating Systems
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ACM Transactions on the Web (TWEB) : Volume 10
ACM Transactions on the Web (TWEB) : Volume 9
ACM Transactions on the Web (TWEB) : Volume 8
ACM Transactions on the Web (TWEB) : Volume 7
ACM Transactions on the Web (TWEB) : Volume 6
Issue 4, November 2012
Issue 3, September 2012
Issue 2, May 2012
Issue 1, March 2012
ClickRank: Learning Session-Context Models to Enrich Web Search Ranking
Identifying Web Spam with the Wisdom of the Crowds
Crawling Ajax-Based Web Applications through Dynamic Analysis of User Interface State Changes
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB) : Volume 5
ACM Transactions on the Web (TWEB) : Volume 4
ACM Transactions on the Web (TWEB) : Volume 3
ACM Transactions on the Web (TWEB) : Volume 2
ACM Transactions on the Web (TWEB) : Volume 1

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Quality and Leniency in Online Collaborative Rating Systems

Content Provider ACM Digital Library
Author Wang, Ke Lauw, Hady W. Lim, Ee-Peng
Copyright Year 2012
Abstract The emerging trend of social information processing has resulted in Web users’ increased reliance on user-generated content contributed by others for information searching and decision making. Rating scores, a form of user-generated content contributed by reviewers in online rating systems, allow users to leverage others’ opinions in the evaluation of objects. In this article, we focus on the problem of summarizing the rating scores given to an object into an overall score that reflects the object’s quality. We observe that the existing approaches for summarizing scores largely ignores the effect of reviewers exercising different standards in assigning scores. Instead of treating all reviewers as equals, our approach models the leniency of reviewers, which refers to the tendency of a reviewer to assign higher scores than other coreviewers. Our approach is underlined by two insights: (1) The leniency of a reviewer depends not only on how the reviewer rates objects, but also on how other reviewers rate those objects and (2) The leniency of a reviewer and the quality of rated objects are mutually dependent. We develop the leniency-aware quality, or $\textit{LQ}$ model, which solves leniency and quality simultaneously. We introduce both an exact and a ranked solution to the model. Experiments on real-life and synthetic datasets show that $\textit{LQ}$ is more effective than comparable approaches. $\textit{LQ}$ is also shown to perform consistently better under different parameter settings.
Starting Page 1
Ending Page 27
Page Count 27
File Format PDF
ISSN 15591131
e-ISSN 1559114X
DOI 10.1145/2109205.2109209
Volume Number 6
Issue Number 1
Journal ACM Transactions on the Web (TWEB)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-03-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Quality Leniency Link analysis Rating Social network mining
Content Type Text
Resource Type Article
Subject Computer Networks and Communications
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