# Principled method for choosing the strength of a prior?

I'm working on an application similar to this one, where the intent is to sort a list of items with ratings according to the best estimate of their average rating. The solution proposed in this link, which may be familiar to many, looks like this:

$(WR) = \frac{vR + mC}{v+m}$

where:

• $R$ = average rating (mean)
• $v$ = number of ratings
• $m$ = parameter signifying the strength of the prior
• $C$ = mean rating over all items

While I can subjectively choose my preferred value of $m$, it seems to me fairly arbitrary. Is there some principled approach for determining this value?

Why are we using these values to rank the items as opposed to the original average ratings? Because we believe that they will better predict future ratings. So let's try to choose $m$ in such a way that we achieve the best predictions.