Graph Neural Networks in Recommender Systems: A Survey

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Additionally, we state new perspectives pertaining to the development of this field.

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• 1 Due to the important application value of recommender systems, there have always been emerging works in this field.
• 2 In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any).
• 3 Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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