  We propose a new hybrid algorithm that allows incorporating both user and
item side information within the standard collaborative filtering technique.
One of its key features is that it naturally extends a simple PureSVD approach
and inherits its unique advantages, such as highly efficient Lanczos-based
optimization procedure, simplified hyper-parameter tuning and a quick
folding-in computation for generating recommendations instantly even in highly
dynamic online environments. The algorithm utilizes a generalized formulation
of the singular value decomposition, which adds flexibility to the solution and
allows imposing the desired structure on its latent space. Conveniently, the
resulting model also admits an efficient and straightforward solution for the
cold start scenario. We evaluate our approach on a diverse set of datasets and
show its superiority over similar classes of hybrid models.
