  In Recommender Systems research, algorithms are often characterized as either
Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained
using a dataset of user preferences while CB algorithms are typically based on
item profiles. These approaches harness different data sources and therefore
the resulting recommended items are generally very different. This paper
presents the CB2CF, a deep neural multiview model that serves as a bridge from
items content into their CF representations. CB2CF is a real-world algorithm
designed for Microsoft Store services that handle around a billion users
worldwide. CB2CF is demonstrated on movies and apps recommendations, where it
is shown to outperform an alternative CB model on completely cold items.
