Recommender Systems have been proved effective to apply knowledge
discovery techniques to the problem of providing personalized
recommendations for products and services to users during a live
interaction. Clustering and trust-based models have been proposed as
improvements to existing recommendations algorithms. While they can
be helpful when used separately, the combination of using social
trust data for clustering users has not been studied adequately so far.

In this work we explore well established clustering schemes, such as
k-means as well as new ones, and we demonstrate the advantages in
performance from the use of social-oriented information for
clustering. The strong points of this approach include the lower
computational cost, the higher resistance to manipulation of user
preferences, as well as the fact that such information can be
provided by the social networks.