DocumentCode
2832347
Title
The Hidden Problem of Uninformed Online Product Reviewers
Author
Lundquist, Doug
Author_Institution
Univ. of Illinois at Chicago, Chicago, IL
fYear
2008
fDate
1-5 Sept. 2008
Firstpage
739
Lastpage
743
Abstract
Online product reviews sometimes display a consistent, positive bias. Prior research has largely focused on reviewer honesty but other possible explanations are lack of product knowledge and self-selection bias. A semantic Web method is proposed to deliver more accurate reviews to online customers. An agent-based model is used to show that such a method can significantly improve information delivery even with noisy knowledge data.
Keywords
customer services; multi-agent systems; retail data processing; semantic Web; agent-based model; information delivery; online customers; product knowledge; reviewer honesty; self-selection bias; semantic Web method; uninformed online product reviewers; Knowledge based systems; Monte Carlo methods; data management; economics; simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Database and Expert Systems Application, 2008. DEXA '08. 19th International Workshop on
Conference_Location
Turin
ISSN
1529-4188
Print_ISBN
978-0-7695-3299-8
Type
conf
DOI
10.1109/DEXA.2008.57
Filename
4624807
Link To Document