  Recommender systems have become an essential tool to help resolve the
information overload problem in recent decades. Traditional recommender
systems, however, suffer from data sparsity and cold start problems. To address
these issues, a great number of recommendation algorithms have been proposed to
leverage side information of users or items (e.g., social network and item
category), demonstrating a high degree of effectiveness in improving
recommendation performance. This Research Commentary aims to provide a
comprehensive and systematic survey of the recent research on recommender
systems with side information. Specifically, we provide an overview of
state-of-the-art recommendation algorithms with side information from two
orthogonal perspectives. One involves the different methodologies of
recommendation: the memory-based methods, latent factor, representation
learning, and deep learning models. The others cover different representations
of side information, including structural data (flat, network, and hierarchical
features, and knowledge graphs); and non-structural data (text, image and video
features). Finally, we discuss challenges and provide new potential directions
in recommendation, along with the conclusion of this survey.
