  In recent years, deep learning has gained an indisputable success in computer
vision, speech recognition, and natural language processing. After its rising
success on these challenging areas, it has been studied on recommender systems
as well, but mostly to include content features into traditional methods. In
this paper, we introduce a generalized neural network-based recommender
framework that is easily extendable by additional networks. This framework
named NHR, short for Neural Hybrid Recommender allows us to include more
elaborate information from the same and different data sources. We have worked
on item prediction problems, but the framework can be used for rating
prediction problems as well with a single change on the loss function. To
evaluate the effect of such a framework, we have tested our approach on
benchmark and not yet experimented datasets. The results in these real-world
datasets show the superior performance of our approach in comparison with the
state-of-the-art methods.
