Collaborative filtering systems make recommendations based on ratings of user preference. Usually, the ratings are unidimensional (e.g. like vs. dislike), and can either explicitly elicited from users or, more typically, are implicitly generated from observations of user behavior. This research examines multi-dimensional or semantic ratings in which a system gets information about the reason behind a preference. Such multidimensional ratings can be projected onto a single dimension, but experiments show that metrics in which the semantic meaning of each rating is taken into account have markedly superior performance.