First of all, it's important to recall that RMSE has the same unit as the dependent variable (DV). It means that there is no absolute good or bad threshold.
However, you can define it based on your DV. For a datum which ranges from 0 to 1000, an RMSE of 0.7 is small, but if the range goes from 0 to 1, it is not that small anymore. However, although the smaller the RMSE, the better, you can make theoretical claims on levels of the RMSE by knowing what is expected from your DV in your field of research. Keep in mind that you can always normalize the RMSE.
Decision support accuracy metrics that are popularly used are Reversal rate, Weighted errors, Receiver Operating Characteristics (ROC) and Precision Recall Curve (PRC), Precision, Recall and F-measure. These metrics help users in selecting items that are of very high quality out of the available set of items.
The metrics view prediction procedure as a binary operation which distinguishes good items from those items that are not good. ROC curves are very successful when performing comprehensive assessments of the performance of some specific algorithms.
Precision is the fraction of recommended items that is actually relevant to the user, while Recall can be defined as the fraction of relevant items that are also part of the set of recommended items. They are computed as --
Precision (P) = Correctly Recommended Items / Total Recommended Items
Recall (R) = Correctly Recommended Items / Total Useful Recommended Items
F-measure defined below helps to simplify precision and recall into a single metric. The resulting value makes comparison between algorithms and across data sets very simple and straightforward.
F-measure = 2PR / P + R
Coverage has to do with the percentage of items and users that a recommender system can provide predictions. Prediction may be practically impossible to make if no users or few users rated an item. Coverage can be reduced by defining small neighborhood sizes.
That's all. Hope it helps!