Two-stage retrieval + ranking architecture, dynamic user embeddings, watch-time optimization, and the elegance of softmax-to-ANN inference

Multi-task learning through specialized expert networks and task-specific gating

Explicit feature crossing and deep learning for improved recommendations

Attention-based recommendation models and dynamic user interest representation

Exploring attention mechanisms, self-attention, and Transformer models

Core DNN concepts including regularization, dropout, and overfitting prevention