  Personalized recommendations are popular in these days of Internet driven
activities, specifically shopping. Recommendation methods can be grouped into
three major categories, content based filtering, collaborative filtering and
machine learning enhanced. Information about products and preferences of
different users are primarily used to infer preferences for a specific user.
Inadequate information can obviously cause these methods to fail or perform
poorly. The more information we provide to these methods, the more likely it is
that the methods perform better. Knowledge graphs represent the current trend
in recording information in the form of relations between entities, and can
provide additional (side) information about products and users. Such
information can be used to improve nearest neighbour search, clustering users
and products, or train the neural network, when one is used. In this work, we
present a new generic recommendation systems framework, that integrates
knowledge graphs into the recommendation pipeline. We describe its software
design and implementation, and then show through experiments, how such a
framework can be specialized for a domain, say movie recommendations, and the
improvements in recommendation results possible due to side information
obtained from knowledge graphs representation of such information. Our
framework supports different knowledge graph representation formats, and
facilitates format conversion, merging and information extraction needed for
training recommendation methods.
