  We introduce EigenRec; a versatile and efficient Latent-Factor framework for
Top-N Recommendations that includes the well-known PureSVD algorithm as a
special case. EigenRec builds a low dimensional model of an inter-item
proximity matrix that combines a similarity component, with a scaling operator,
designed to control the influence of the prior item popularity on the final
model. Seeing PureSVD within our framework provides intuition about its inner
workings, exposes its inherent limitations, and also, paves the path towards
painlessly improving its recommendation performance. A comprehensive set of
experiments on the MovieLens and the Yahoo datasets based on widely applied
performance metrics, indicate that EigenRec outperforms several
state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation
accuracy, exhibiting low susceptibility to sparsity, even in its most extreme
manifestations -- the Cold-Start problems. At the same time EigenRec has an
attractive computational profile and it can apply readily in large-scale
recommendation settings.
