  Among the machine learning applications to business, recommender systems
would take one of the top places when it comes to success and adoption. They
help the user in accelerating the process of search while helping businesses
maximize sales. Post phenomenal success in computer vision and speech
recognition, deep learning methods are beginning to get applied to recommender
systems. Current survey papers on deep learning in recommender systems provide
a historical overview and taxonomy of recommender systems based on type. Our
paper addresses the gaps of providing a taxonomy of deep learning approaches to
address recommender systems problems in the areas of cold start and candidate
generation in recommender systems. We outline different challenges in
recommender systems into those related to the recommendations themselves
(include relevance, speed, accuracy and scalability), those related to the
nature of the data (cold start problem, imbalance and sparsity) and candidate
generation. We then provide a taxonomy of deep learning techniques to address
these challenges. Deep learning techniques are mapped to the different
challenges in recommender systems providing an overview of how deep learning
techniques can be used to address them. We contribute a taxonomy of deep
learning techniques to address the cold start and candidate generation problems
in recommender systems. Cold Start is addressed through additional features
(for audio, images, text) and by learning hidden user and item representations.
Candidate generation has been addressed by separate networks, RNNs,
autoencoders and hybrid methods. We also summarize the advantages and
limitations of these techniques while outlining areas for future research.
