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Recommender Systems (RS) are widely used to provide users with
personalized suggestions taken from an extended variety of items.
One of the major challenges of RS is the accuracy in cold-start
situations where little feedback is available for a user or an item.
Exploiting available user and item metadata helps to cope with this
problem. We propose a hybrid training framework consisting of two
predictors, a collaborative filtering instance and a metadata-based
instance relying on content and demographic data. Our framework
supports a wide range of algorithms to be used as predictors. The
cross-training mechanism we design minimizes the weaknesses of
one instance by updating its training with predicted data from the
other instance. A sophisticated sampling function selects ratings to
be predicted for cross-training
We evaluate our framework conducting multiple experiments
on the MovieLens 100K dataset, simulating different scenarios in-
cluding user and item cold-start. Our framework outperforms state-
of-the-art algorithms and is able to provide accurate predictions
across all tested scenarios.