Equivalent of Bayesian average for unary rating system

Question

I am really looking forward to implement bayesian average rating system for a site I'm developing. I have faced a problem though - all of the examples I can find on the net, are for multi-value rating systems, with the smallest being binary - likes / dislikes (Apply Bayesian average in a NON 5-star rating system).

I cannot seem to understand how I could apply binary bayesian to a unary rating system.

I have no dislikes, I have only likes.

Given the algorithm:

  (n / (n + C)) * j + (C / (n + C)) * m

• C is the average number of ratings an item receives
• m is the average rating across all items
• n is the number of ratings the current item
• j is the average rating for the current item

I get stuck on m - the average rating accross all items. The average rating is 1 for everything.

How do I tweak this formula for unary rating system?

Maybe there are other, better suited equivalents of bayesian for such task?

Answer 1

Number of likes is a one-dimensional input, so it's hard to do anything interesting without another input. Two possibilities are how old the item is and how many users have viewed it.