Estimating Online Review Helpfulness with Probabilistic Distribution and Confidence

Autor:	Guoqing Chen, Zunqiang Zhang, Qiang Wei
Rok vydání:	2013
Předmět:	business.industry Computer science media_common.quotation_subject Machine learning computer.software_genre Confidence interval Possibility distribution Product reviews Voting Helpfulness Artificial intelligence Decision process Construct (philosophy) business computer media_common
Zdroj:	Foundations and Applications of Intelligent Systems ISBN: 9783642378287
DOI:	10.1007/978-3-642-37829-4_35
Popis:	Product review helpfulness information is useful knowledge for consumers in their online shopping decision processes. Unlike the traditional method using the simple voting percentages, this paper proposes a new method for estimating the degrees of helpfulness with two features. One is to take into account the helpfulness distribution information on all reviews of concern in determination of helpfulness degrees; the other is to construct confidence intervals (CIs) of helpfulness to distinguish different reviews with the same voting percentage. Both synthetic and real data experiments, along with an illustrative example, reveal that the proposed method is superior to the traditional one in light of estimation accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::be15b7fd920ad002762c6e9645d3ee50 https://doi.org/10.1007/978-3-642-37829-4_35 Zobrazit plný text záznamu