将商店绩效与贝叶斯平均评分进行比较

问：

我正在尝试根据商店的评级和评论来比较商店的表现，我在类似的帖子中遇到了两种方法——加权评级和贝叶斯评级 按 5 星评级排序的更好方法是什么？

我的数据集有点相似，商店有总体评分（满分 5 星评分）和评论数。但是，有些商店的评分较高，评论较少，有些商店的评分较高，评论较高，而另一些商店的评分较低，评论较高。我很难理解加权评级方法中的“m”是什么意思，该方法以及埃文·米勒的贝叶斯公式（即]1）weighted rating = (v / (v + m)) * R + (m / (v + m)) * C

nk is the number of k-star ratings,
sk is the "worth" (in points) of k stars,
N is the total number of votes
K is the maximum number of stars (e.g. K=5, in a 5-star rating system)
z_alpha/2 is the 1 - alpha/2 quantile of a normal distribution. If you want 95% confidence (based on the Bayesian posterior distribution) that the actual sort criterion is at least as big as the computed sort criterion, choose z_alpha/2 = 1.65```

Below is a sample dataset to provide more clarity. The ratings lie between 3.5 to 4.6 with reviews ranging from ~200 to ~2800. Which of the above two methods should be a good fit in my case and how can I use the variables in my dataset in the above two formulae?