  Recommender Systems are a subclass of machine learning systems that employ
sophisticated information filtering strategies to reduce the search time and
suggest the most relevant items to any particular user. Hybrid recommender
systems combine multiple recommendation strategies in different ways to benefit
from their complementary advantages. Some hybrid recommender systems have
combined collaborative filtering and content-based approaches to build systems
that are more robust. In this paper, we propose a hybrid recommender system,
which combines Alternating Least Squares (ALS) based collaborative filtering
with deep learning to enhance recommendation performance as well as overcome
the limitations associated with the collaborative filtering approach,
especially concerning its cold start problem. In essence, we use the outputs
from ALS (collaborative filtering) to influence the recommendations from a Deep
Neural Network (DNN), which combines characteristic, contextual, structural and
sequential information, in a big data processing framework. We have conducted
several experiments in testing the efficacy of the proposed hybrid architecture
in recommending smartphones to prospective customers and compared its
performance with other open-source recommenders. The results have shown that
the proposed system has outperformed several existing hybrid recommender
systems.
