Recent Advances in Heterogeneous Relation Learning for Recommendation

Understand

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.