  Collaborative filtering models generally perform better than content-based
filtering models and do not require careful feature engineering. However, in
the cold-start scenario collaborative information may be scarce or even
unavailable, whereas the content information may be abundant, but also noisy
and expensive to acquire. Thus, selection of particular features that improve
cold-start recommendations becomes an important and non-trivial task. In the
recent approach by Nembrini et al., the feature selection is driven by the
correlational compatibility between collaborative and content-based models. The
problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO)
which, due to its NP-hard complexity, is solved using Quantum Annealing on a
quantum computer provided by D-Wave. Inspired by the reported results, we
contend the idea that current quantum annealers are superior for this problem
and instead focus on classical algorithms. In particular, we tackle QUBO via
TTOpt, a recently proposed black-box optimizer based on tensor networks and
multilinear algebra. We show the computational feasibility of this method for
large problems with thousands of features, and empirically demonstrate that the
solutions found are comparable to the ones obtained with D-Wave across all
examined datasets.
