  Recommendations Systems allow users to identify trending items among a
community while being timely and relevant to the user's expectations. When the
purpose of various Recommendation Systems differs, the required type of
recommendations also differs for each use case. While one Recommendation System
may focus on recommending popular items, another may focus on recommending
items that are comparable to the user's interests. Content-based filtering,
user-to-user & item-to-item Collaborative filtering, and more recently; Deep
Learning methods have been brought forward by the researchers to achieve better
quality recommendations.
  Even though each of these methods has proven to perform well individually,
there have been attempts to push the boundaries of their limitations. Following
a wide range of methods, researchers have tried to expand on the capabilities
of standard recommendation systems to provide the most effective
recommendations to users while being more profitable from a business's
perspective. This has been achieved by taking a hybrid approach when building
models and architectures for Recommendation Systems.
  This paper is a review of the novel models & architectures of hybrid
Recommendation Systems. The author identifies possibilities of expanding the
capabilities of baseline models & the advantages and drawbacks of each model
with selected use cases in this review.
