  An Item based recommender system works by computing a similarity between
items, which can exploit past user interactions (collaborative filtering) or
item features (content based filtering). Collaborative algorithms have been
proven to achieve better recommendation quality then content based algorithms
in a variety of scenarios, being more effective in modeling user behaviour.
However, they can not be applied when items have no interactions at all, i.e.
cold start items. Content based algorithms, which are applicable to cold start
items, often require a lot of feature engineering in order to generate useful
recommendations. This issue is specifically relevant as the content descriptors
become large and heterogeneous. The focus of this paper is on how to use a
collaborative models domain-specific knowledge to build a wrapper feature
weighting method which embeds collaborative knowledge in a content based
algorithm. We present a comparative study for different state of the art
algorithms and present a more general model. This machine learning approach to
feature weighting shows promising results and high flexibility.
