Personalization and Ranking Systems Questions

Designing personalization and ranking architectures that operate at very large scale. Candidates should cover candidate generation and ranking pipelines, offline and real time feature engineering, feature stores, model training and serving, learning to rank approaches, latency and freshness tradeoffs, using in memory structures such as prefix tries for fast type ahead, experimentation and A B testing infrastructure, online evaluation and feedback loops, and data privacy and governance concerns.