Recommendation Systems (7): Graph Neural Networks and Social Recommendation

Fri, 19 Dec 2025 09:00:00 +0000

When Netflix decides what to recommend next, it does not look at your watch history in isolation. Behind the scenes there is a web of relationships: movies that share actors, users with overlapping taste, ratings that ripple through the catalogue. The “graph” view is not a metaphor — every interaction matrix is a graph, and treating it as one unlocks ideas that flat user/item embeddings cannot express.

Graph neural networks (GNNs) are the tool that lets us reason over that graph. Instead of learning each user and each item in isolation, a GNN says: your representation is shaped by the company you keep. That single shift powers Pinterest’s billion-node PinSage, the strikingly simple LightGCN that beats heavier baselines on collaborative filtering, and the social-recommendation systems that fuse “what you watched” with “what your friends watched.”

Social Recommendation on Chen Kai Blog

Recommendation Systems (7): Graph Neural Networks and Social Recommendation