[HN Gopher] Normalizing Ratings
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       Normalizing Ratings
        
       Author : Symmetry
       Score  : 16 points
       Date   : 2025-05-02 00:39 UTC (22 hours ago)
        
 (HTM) web link (hopefullyintersting.blogspot.com)
 (TXT) w3m dump (hopefullyintersting.blogspot.com)
        
       | nlh wrote:
       | Similarly - one of my biggest complaints about almost every
       | rating system in production is how just absolutely lazy they are.
       | And by that, I mean everyone seems to think "the object's
       | collective rating is an average of all the individual ratings" is
       | good enough. It's not.
       | 
       | Take any given Yelp / Google / Amazon page and you'll see some
       | distribution like this:
       | 
       | User 1: "5 stars. Everything was great!"
       | 
       | User 2: "5 stars. I'd go here again!"
       | 
       | User 3: "1 star. The food was delicious but the waiter was so
       | rude!!!one11!! They forgot it was my cousin's sister's mother's
       | birthday and they didn't kiss my hand when I sat down!! I love
       | the food here but they need to fire that one waiter!!"
       | 
       | Yelp: 3.6 stars average rating.
       | 
       | One thing I always liked about FourSquare was that they did NOT
       | use this lazy method. Their score was actually intelligent - it
       | checked things like how often someone would return, how much time
       | they spent there, etc. and weighted a review accordingly.
        
         | theendisney wrote:
         | With averages: to have 5 stars you need a hudred 5 star ratings
         | for each one star rating.
         | 
         | If one would normalize the ratings they could change without
         | doing anything. A former customer may start giving good ratings
         | elsewhere making yours worse or give poor ones inproving yours.
         | 
         | Maybe the relevance of old ratings should decline.
        
           | kayson wrote:
           | The normalization doesn't have to be "live". You could apply
           | the factor at time of rating and then not change it.
        
       | xnx wrote:
       | I don't understand why letter grades aren't more popular for
       | rating things in the US.
       | 
       | "A+" "B" "C-" "F", etc. feel a lot more intuitive than how stars
       | are used.
        
         | NegativeK wrote:
         | We'd still get the same pressure to give an A+ to every
         | interaction unless things were fucked.
         | 
         | I used to rate three stars for what "performs as expected"
         | until I realized that it's punishing good products. Switch to
         | A-F would result in the same behavior, except it'd be Uber
         | drivers trying to make a living instead of noxious parents
         | declaring that their kid deserves an A.
        
       | Retr0id wrote:
       | > I'm genuinely mystified why its not applied anywhere I can see.
       | 
       | I wonder if companies are afraid of being accused of "cooking the
       | books", especially in contexts where the individual ratings are
       | visible.
       | 
       | If I saw a product with 3x 5-star reviews and 1x 3-star review,
       | I'd be suspicious if the overall rating was still a perfect 5
       | stars.
        
       | tibbar wrote:
       | One of my favorite algorithms for this is Expectation
       | Maximization [0].
       | 
       | You would start by estimating each driver's rating as the average
       | of their ratings - and then estimate the bias of each rider by
       | comparing the average rating they give to the estimated score of
       | their drivers. Then you repeat the process iteratively until you
       | see both scores (driver rating, and user bias) converge.)
       | 
       | [0]
       | https://en.wikipedia.org/wiki/Expectation%E2%80%93maximizati...
        
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       (page generated 2025-05-02 23:00 UTC)