  Recommender systems have played a critical role in many web applications to
meet user's personalized interests and alleviate the information overload. In
this survey, we review the development of recommendation frameworks with the
focus on heterogeneous relational learning, which consists of different types
of dependencies among users and items. The objective of this task is to map
heterogeneous relational data into latent representation space, such that the
structural and relational properties from both user and item domain can be well
preserved. To address this problem, recent research developments can fall into
three major lines: social recommendation, knowledge graph-enhanced recommender
system, and multi-behavior recommendation. We discuss the learning approaches
in each category, such as matrix factorization, attention mechanism and graph
neural networks, for effectively distilling heterogeneous contextual
information. Finally, we present an exploratory outlook to highlight several
promising directions and opportunities in heterogeneous relational learning
frameworks for recommendation.
