  The purpose of this master's thesis is to study and develop a new algorithmic
framework for collaborative filtering (CF) to generate recommendations. The
method we propose is based on the exploitation of the hierarchical structure of
the item space and intuitively "stands" on the property of Near Complete
Decomposability (NCD) which is inherent in the structure of the majority of
hierarchical systems. Building on the intuition behind the NCDawareRank
algorithm and its related concept of NCD proximity, we model our system in a
way that illuminates its endemic characteristics and we propose a new
algorithmic framework for recommendations, called HIR. We focus on combining
the direct with the NCD "neighborhoods" of items to achieve better
characterization of the inter-item relations, in order to improve the quality
of recommendations and alleviate sparsity related problems.
