The Good, the Bad and the Picky: Consumer Heterogeneity and the Reversal of Product Ratings
研究了消费者经验差异如何导致在线评分偏差，发现经验用户评分更严格且更偏好高质量电影，导致评分压缩和排名反转，并提出一种去偏算法。
We study the impact of consumer heterogeneity on online ratings. Consumers differ in their experience, which can affect both their choices and ratings. Thus, biases in average ratings can arise when the opinions of experienced and novice users are aggregated. We first build a two-period model to characterize the biases’ drivers and consequences. We test our theory combining data from IMDb and MovieLens, two well-known movie ratings platforms. We proxy users’ experience with the total number of ratings posted on the platforms. First, using external measures of quality, such as the Academy awards and nominations, we show that, on both platforms, experienced users, on average, rate movies of higher quality compared with novices. Moreover, they post more stringent ratings than novices for more than 98% of movies. Combined, these imply a compression in aggregate ratings, and thus a bias against high quality movies. We then propose a simple, fixed-point algorithm to debias ratings. Our debiased ratings demonstrate the presence of ranking reversals for more than 8% of comparisons in our sample. As a result, our debiased ratings better correlate with external measures of quality. This paper was accepted by Eric Anderson, marketing. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03281 .