  We propose a tensor-based model that fuses a more granular representation of
user preferences with the ability to take additional side information into
account. The model relies on the concept of ordinal nature of utility, which
better corresponds to actual user perception. In addition to that, unlike the
majority of hybrid recommenders, the model ties side information directly to
collaborative data, which not only addresses the problem of extreme data
sparsity, but also allows to naturally exploit patterns in the observed
behavior for a more meaningful representation of user intents. We demonstrate
the effectiveness of the proposed model on several standard benchmark datasets.
The general formulation of the approach imposes no restrictions on the type of
observed interactions and makes it potentially applicable for joint modelling
of context information along with side data.
