  When users rate objects, a sophisticated algorithm that takes into account
ability or reputation may produce a fairer or more accurate aggregation of
ratings than the straightforward arithmetic average. Recently a number of
authors have proposed different co-determination algorithms where estimates of
user and object reputation are refined iteratively together, permitting
accurate measures of both to be derived directly from the rating data. However,
simulations demonstrating these methods' efficacy assumed a continuum of rating
values, consistent with typical physical modelling practice, whereas in most
actual rating systems only a limited range of discrete values (such as a 5-star
system) is employed. We perform a comparative test of several co-determination
algorithms with different scales of discrete ratings and show that this
seemingly minor modification in fact has a significant impact on algorithms'
performance. Paradoxically, where rating resolution is low, increased noise in
users' ratings may even improve the overall performance of the system.
