title	Bayescore
emoji	🚀
colorFrom	pink
colorTo	blue
sdk	gradio
sdk_version	5.49.0
app_file	app.py
pinned	false
short_description	Bayesian approach to review sorting for Places API

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Outline

Approach inspired by Bayesian probability is to have an initial belief about the 'true' rating of an item, and use user ratings to update this belief, meaning we will have to quantify an initial rating for each item (using a 5 point scale, as most review systems do).

As such, we require 2 parameters:

• The 'true' default rating of an item, if there are no existing ratings 'R' -> the initial belief

• The weight that we want to give this initial belief, versus subsequent ratings 'W' -> the weight of our initial belief, where belief 'R' is worth W user ratings

Examples

Assume you have W ratings of value R along with any user ratings, and find the average. Given R=2 and W=3, where N is number of user ratings, V is their average

Weighted_Rating = (R*W + N*V) / (W+N)

1. For 100 user ratings of 4

• Weighted_Rating = (2 ⋅ 3 + 100 ⋅ 4) / (100 + 3) = 3.94☆
• In comparison, a straight average would give us a 4☆ rating.

2. For 3 ratings of 5 and 1 rating of 4

• Weighted_Rating = (2 ⋅ 3 + 1 ⋅ 4 + 3 ⋅ 5) / (3 + 4) = 3.57☆
• Average = 4.75☆

3. For 10 ratings of 4

• Weighted_Rating = (2 ⋅ 3 + 10 ⋅ 4) / (3 + 10) = 3.54☆
• Average = 4☆

4. For 1 ratings of 5

• Weighted_Rating = (2 ⋅ 3 + 5 ⋅ 1) / (3 + 1) = 2.75☆
• Average = 5☆