[T_S_]

tl;dr (my interpretation anyway). In utility theory a completely specified preference ordering is the starting point and a utility function can be derived to represent it. These functions are unique only up to a monotone transformation. In the recommender systems we take the quantity as a given and infer the preference order for missing items. If you reassign assign all the 5 stars items to 10 stars, it is perfectly consistent with the ordering, but your inference method may be (will be!) sensitive to such reassignments. To me this suggests you should also be estimating the shape of the best transformation of the rating system. Think of it as an opportunity to reduce bias. The next step would be to see if your particular decision problem is sensitive to such parameters. If not, chuck those parameters and go about your business.

[dr_strongarm]

I think 11 stars is a better scale.

Additionally, a great body of work in behavioral psych tells us that humans have a tough time measuring preferences on any absolute scale; however, we can consistently compare two items as better or worse (particularly when they're of the same type, instead of apples versus oranges). "Riffle independence" is a recent method for modeling these kinds of preference distributions, and has been used quite successfully for social curation of the blogosphere - i.e., showing the best set of blogs that span the topic space and have little redundancy.