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Published in:
2011 | OriginalPaper | Chapter
Toward a Fair Review-Management System
Authors : Theodoros Lappas, Evimaria Terzi
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
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Item reviews are a valuable source of information for potential buyers, who are looking for information on a product’s attributes before making a purchase decision. This search of information is often hindered by overwhelming numbers of available reviews, as well as low-quality and noisy content. While a significant amount of research has been devoted to filtering and organizing review corpora toward the benefit of the buyers, a crucial part of the reviewing process has been overlooked:
reviewer satisfaction
. As in every content-based system, the content-generators, in this case the reviewers, serve as the driving force. Therefore, keeping the reviewers satisfied and motivated to continue submitting high-quality content is essential. In this paper, we propose a system that helps potential buyers by focusing on high-quality and informative reviews, while keeping reviewers content and motivated.