Explore the architecture and training behind the two-tower model of a product recommender built using Red Hat OpenShift AI.

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The two-tower model architecture uses dual neural networks to create shared embedding spaces for users and products, enabling product recommendations. The training pipeline leverages OpenShift AI with KFP to orchestrate data loading, model training, and candidate generation stages. Each tower processes entity features through feedforward networks, producing 64-component embeddings that are compared using Euclidean distance against interaction magnitude scores. The architecture handles cold start problems for new users through category-based preferences and addresses data sharing challenges between pipeline components using S3-compatible storage.

Understanding the recommender system's two-tower model

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