  Cold-start is a very common and still open problem in the Recommender Systems
literature. Since cold start items do not have any interaction, collaborative
algorithms are not applicable. One of the main strategies is to use pure or
hybrid content-based approaches, which usually yield to lower recommendation
quality than collaborative ones. Some techniques to optimize performance of
this type of approaches have been studied in recent past. One of them is called
feature weighting, which assigns to every feature a real value, called weight,
that estimates its importance. Statistical techniques for feature weighting
commonly used in Information Retrieval, like TF-IDF, have been adapted for
Recommender Systems, but they often do not provide sufficient quality
improvements. More recent approaches, FBSM and LFW, estimate weights by
leveraging collaborative information via machine learning, in order to learn
the importance of a feature based on other users opinions. This type of models
have shown promising results compared to classic statistical analyzes cited
previously. We propose a novel graph, feature-based machine learning model to
face the cold-start item scenario, learning the relevance of features from
probabilities of item-based collaborative filtering algorithms.
