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

The effect of discrete vs. continuous-valued ratings on reputation and ranking systems