Decrease Product Rating Uncertainty Through Focused Reviews Solicitation

Customer reviews are an essential resource to reduce an online product's uncertainty, which has been shown to be a critical factor for its purchase decision. Existing e-commerce platforms typically ask users to write free-form text reviews, which are sometimes augmented by a small set of predefined questions, e.g. "rate the product description's accuracy from 1 to 5." In this paper, we argue that this "passive" style of review solicitation is suboptimal in achieving low-uncertainty "review profiles" for products. Its key drawback is that some product aspects receive a very large number of reviews while other aspects do not have enough reviews to draw confident conclusions. Therefore, we hypothesize that we can achieve lower-uncertainty review profiles by carefully selecting which aspects users are asked to rate. To test this hypothesis, we propose various techniques to dynamically select which aspects to ask users to rate given the current review profile of a product. We use Bayesian inference principles to define reasonable review profile uncertainty measures; specifically, via an aspect's rating variance. We compare our proposed aspect selection techniques to several baselines on several review profile uncertainty measures. Experimental results on two real-world datasets show that our methods lead to better review profile uncertainty compared to aspect selection baselines and traditional passive review solicitations. Moreover, we present and evaluate a hybrid solicitation method that combines the advantages of both active and passive review solicitations.