Abstract
Given the sharply increasing number of online reviews, the selection of strategies by review-hosting firms to help users access more helpful reviews is an intriguing but insufficiently studied issue. We first propose a model to help us understand how reviews receive helpful votes (HV) and non-helpful votes. According to this model, the performances of different ranking approaches are compared using several simulated datasets with empirical features. In addition to three well-known ranking approaches, we develop a novel approach based on Bayesian statistics that is easy to implement in existing websites and can be combined with other content recommendation techniques to determine the prior belief in online reviews. More importantly, we suggest two simple ways to enhance existing ranking approaches. The numerical evidence demonstrates the advantages of two enhanced approaches, as indicated by higher helpful ratios and a reduced Matthew effect. These findings have important practical implications for consumers, online retailers, and review-hosting firms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.