  Neighborhood-based collaborative filtering algorithms usually adopt a fixed
neighborhood size for every user or item, although groups of users or items may
have different lengths depending on users' preferences. In this paper, we
propose an extension to a non-personalized recommender based on confidence
intervals and hierarchical clustering to generate groups of users with optimal
sizes. The evaluation shows that the proposed technique outperformed the
traditional recommender algorithms in four publicly available datasets.
