总结贝叶斯评级公式

发布于 2024-09-08 03:28:46 字数 390 浏览 7 评论 0 原文

根据这个网址，我发现贝叶斯评级，它很好地解释了评级模型，我想总结一下这个公式，以便任何人都可以更轻松地实现 SQL 语句。如果我这样总结公式，这是否正确？

avg_num_votes  = Sum(votes)/Count(votes) * Count(votes)

avg_rating     = sum(votes)/count(votes)

this_num_votes = count(votes)

this_rating    = Positive_votes - Negative_votes

迦特

原文

Based on this url i found Bayesian Rating, which explains the rating model very well, i wanted to summarize the formula to make it much easier for anyone implementing an SQL statement. Would this be correct if i summarized the formula like this?

avg_num_votes  = Sum(votes)/Count(votes) * Count(votes)

avg_rating     = sum(votes)/count(votes)

this_num_votes = count(votes)

this_rating    = Positive_votes - Negative_votes

Gath

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你对谁都笑 2024-09-15 03:28:46

它看起来更像这样：

avg_num_votes  = Count(votes)/Count(items with at least 1 vote)
avg_rating     = Sum(votes)/Count(items with at least 1 vote)

this_num_votes = Count(votes for this item)
this_rating    = Sum(votes for this item)/Count(votes for this item)

如果您使用简单的 +/- 系统，Sum(votes) = Count(positive votes) （即，将 + 视为 1，- 视为 0）

另请参阅：贝叶斯平均值。

It would look more like this:

avg_num_votes  = Count(votes)/Count(items with at least 1 vote)
avg_rating     = Sum(votes)/Count(items with at least 1 vote)

this_num_votes = Count(votes for this item)
this_rating    = Sum(votes for this item)/Count(votes for this item)

If you are using a simple +/- system, Sum(votes) = Count(positive votes) (ie. treat + as 1, - as 0)