How do modern recommendation systems use unsupervised learning architectures?

They use embedding learning and similarity search to recommend items.

User and item embeddings are learned using unsupervised or self-supervised objectives such as co-occurrence prediction, matrix factorization, or contrastive learning. These embeddings are stored in vector databases and queried via ANN systems for recommendations.