Separate durable preference from noise by designing robust feature representations. Model temporal dynamics and changing tastes with sequential and recency-aware systems. Solve the cold-start problem using cohort signals, clustering, and content embeddings. Bridge ML and constrained optimization by integrating model scores with operations-research engines. Advance the modeling with modern, high-scale personalization architectures. Drive rigorous experimentation through robust offline evaluation metrics and online A/B tests.