Targeted Solicitation of Product Reviews
Nhat X. T. Le, Ryan Rivas, James M. Flegal, Vagelis Hristidis · 2019
Customer reviews have become an essential resource when people search for goods or services on the Internet. Previous work has shown that reducing a product's uncertainty is critical to its purchase decision. Thus, reviews are more effective when they reduce a product's uncertainty. 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 principles to define reasonable review profile uncertainty measures; specifically, we apply Bayesian inference to measure 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.