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.

本文综述了基于用户或物品侧面信息的推荐系统研究，并从方法论和侧面信息表示两个角度，概括了基于记忆、潜在因子、表示学习和深度学习等不同方法的最新推荐算法。同时阐述了结构化数据和非结构化数据的侧面信息表示，并探讨了应用推荐系统面临的挑战和未来发展方向。