  Making recommendations in the presence of sparsity is known to present one of
the most challenging problems faced by collaborative filtering methods. In this
work we tackle this problem by exploiting the innately hierarchical structure
of the item space following an approach inspired by the theory of
Decomposability. We view the itemspace as a Nearly Decomposable system and we
define blocks of closely related elements and corresponding indirect proximity
components. We study the theoretical properties of the decomposition and we
derive sufficient conditions that guarantee full item space coverage even in
cold-start recommendation scenarios. A comprehensive set of experiments on the
MovieLens and the Yahoo!R2Music datasets, using several widely applied
performance metrics, support our model's theoretically predicted properties and
verify that NCDREC outperforms several state-of-the-art algorithms, in terms of
recommendation accuracy, diversity and sparseness insensitivity.
