Title :
The Hidden Problem of Uninformed Online Product Reviewers
Author_Institution :
Univ. of Illinois at Chicago, Chicago, IL
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;
Conference_Titel :
Database and Expert Systems Application, 2008. DEXA '08. 19th International Workshop on
Conference_Location :
Turin
Print_ISBN :
978-0-7695-3299-8
DOI :
10.1109/DEXA.2008.57
