如何根据分数标准化评论 [英] How To Normalize Reviews Based On Score

问题描述

使评论正常化的最佳方法是什么?IE.让我们假设我们有用户可以从 1 到 5 颗星中投票的产品.

What is the best way to normalize reviews? I.E. lets assume we have products that users can vote from 1-5 stars.

简单地取平均值不是一个好方法，因为它没有考虑评论数量.

Simply taking the average is not a good way, because it does not account for the number of reviews.

例如，如果一个产品只有一个 5 星的评论，它不应该领先于一个有 10000 个评论的产品，仅仅因为唯一的评论给了它 5 颗星.

For example, if a product only has one review of a 5 star, it should not be ahead of a product with 10000 reviews, simply because the only review gave it 5 stars.

基本上，我如何根据评论数量对分数进行标准化?

Essentially how do I normalize the score based on the number of reviews as well?

推荐答案

如果我的回答看起来很疯狂，我很抱歉.但是当我第一次看到你的问题时，我想到了以下答案.

I am sorry if my answer looks crazy. But when I first saw your question, the following answer came to my mind.

计算最受好评的 250 个标题的公式给出了一个真实的贝叶斯估计:

The formula for calculating the Top Rated 250 Titles gives a true Bayesian estimate:

weighted rating (WR) = (v ÷ (v+m)) × R + (m ÷ (v+m)) × C 

哪里:

R = 电影的平均值(平均值)=(评分)

R = average for the movie (mean) = (Rating)

v = 电影的投票数 = (votes)

v = number of votes for the movie = (votes)

m = 进入前 250 名所需的最低票数(目前3000)

m = minimum votes required to be listed in the Top 250 (currently 3000)

C = 整个报告的平均投票(目前为 6.9)

C = the mean vote across the whole report (currently 6.9)

(这是 IMDB 根据用户评论和投票对他们的顶级电影进行排名的方式.以下是我获得上述段落的页面的链接:http://www.imdb.com/chart/top.)

(This is how IMDB ranks their top films according to user reviews and votes. Below is a link to the page where I got the above passage: http://www.imdb.com/chart/top.)

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