Learning from positive and unlabeled amazon reviews: towards identifying trustworthy reviewers.
WWW 2012: 21st World Wide Web Conference 2012 Lyon France April, 2012(2012)
摘要
On-line marketplaces have been growing in importance over the last few years. In such environments, reviews consist the main reputation mechanism for the available products. Hence, presenting high quality reviews is crucial in achieving a high level of customer satisfaction. Towards this direction, in this work, we introduce a new dimension of review quality, the reviewer's "trustfulness". We assume that voluntary information provided by Amazon reviewers, regarding whether they are the actual buyers of the product, signals the reliability of a review. Based on this information, we characterize a reviewer as trustworthy (positive instance) or of unknown "trustfulness" (unlabeled instance). Then, we build models that exploit reviewers' profile information and on-line behavior to rank them according to the probability of being trustworthy. Our results are very promising, since they provide evidence that our predictive models separate positive from unlabeled instances with very high accuracies.
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