  Similarity measures play a fundamental role in memory-based nearest neighbors
approaches. They recommend items to a user based on the similarity of either
items or users in a neighborhood. In this paper we argue that, although it
keeps a leading importance in computing recommendations, similarity between
users or items should be paired with a value of dissimilarity (computed not
just as the complement of the similarity one). We formally modeled and injected
this notion in some of the most used similarity measures and evaluated our
approach showing its effectiveness in terms of accuracy results.
