I don't think you have to resort to any overly complex machinery to achieve similar behavior. The simplest approach is to just use a non uniform prior. His pessimistic bound could be emulated by having an initial alpha that places more weight on low star ratings. The intuitive interpretation of that being "things are probably bad unless proven good" roughly. Another option would be to generate the prior based on the posterior distributions of other items. Just take the distribution of ratings observations for all products of a given type (perhaps only items produced by that company?) to get a sensible prior on a new item in that category.

The strength of priors here is that it is very easy to take intuitions and encode them statistically, in an understandable way. Taking the lower bound of a test statistic doesn't admit much in the way of intuition.