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| Content Provider | ACM Digital Library |
|---|---|
| Author | Feng, Wei Huang, Shen Zhang, Yongzheng Shen, Dan Baudin, Catherine |
| Abstract | With the number of online reviews growing rapidly, it is increasingly difficult to digest all the information within limited time. To help users efficiently get concise information about a product, researchers have studied algorithms for automated opinion summarization. However, users might expect to further read detailed high-quality reviews in addition to a review outline. This raises another interesting problem not well studied yet: how to discover high quality product reviews? Previous research examined various properties of a product review to predict its quality. In this paper, we further explore this topic by incorporating another information resource: the behavior of review authors in an e-commerce community. First, we perform a high-level analysis on two kinds of data: product reviews and deal transactions. According to the results of this analysis, three features, including personal reputation, seller degree and expertise degree, are studied to assess the quality of a review from a credibility and expertise perspective. Our analysis shows that these features are strongly related to review quality and that they can help uncover review spamming by sellers. Furthermore, we propose a simulation model based on the above findings. The model is able to generate the basic properties of the review community, especially when the above three features are taken into account. |
| Starting Page | 133 |
| Ending Page | 141 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781605585864 |
| DOI | 10.1145/1593254.1593274 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-08-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Product review quality User behavior modeling E-commerce |
| Content Type | Text |
| Resource Type | Article |
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