Bayesian ranking of items with up and downvotes or 5 star ratings (2015)
Not a statistician, but this still seems flawed.  The pretend votes need to be related to the person seeing the list of items.   These normally come from the population (i.e. if you were ranking Netflix, the pretend votes would be the sum of all votes that exist for every movie, grouped by star count).  This makes sense, because if you had no other information, your guess would just be the average of all the existing ratings.The problem is that the pretend votes need to be culled in order to be predictive.  Otherwise they dominate in the arithmetic.  They need to be more specific to the user looking at the ranking.  Continuing with the Netflix example, if a user was looking for scary movies, the pretend votes need to come from the corpus of all scary movies, rather than all movies that exist.Here's the problem, there doesn't seem to be a good way to narrow the pretend votes.  Worse, there isn't a good way to combine the two.   If the pretend votes came from two sources, its not clear what to do.    For example, if the user is from California, the California pretend votes (priors?) need to be combined with the scary movie pretend votes.How can we add pretend votes without justifying where they came from?