I started with objectives which felt right, but I spent way too long debating engagement signals versus satisfaction signals before actually getting to any architecture.
Start by clarifying the product goal and constraints (e.g., maximize long-term user engagement while balancing freshness, diversity, and integrity). Then outline a multi-stage ranking system (candidate generation, ranking, re-ranking) and discuss model choices (e.g., GBDT, DNN, multi-task learning) with evaluation metrics (offline: NDCG, AUC; online: A/B tests on engagement and satisfaction).
Pro tip: Emphasize the importance of defining the right objective (e.g., long-term user value over short-term clicks) and mention how you'd handle position bias and feedback loops in training data.
Define the ranking problem: what to rank (posts), for whom (users), and the goal (e.g., maximize meaningful interactions). Clarify constraints like latency, scalability, and content freshness.
Identify data sources: user features (demographics, past behavior), post features (content type, author, freshness), and context (time, device). Discuss feature engineering and handling of missing data.
Propose a multi-stage system: candidate generation (e.g., collaborative filtering, embeddings) to narrow down, then a ranking model (e.g., deep neural network with multi-task learning) to predict engagement probabilities, and finally a re-ranking layer for diversity and business rules.
Describe training setup: loss functions (e.g., logistic loss for CTR, pairwise for ranking), handling position bias (e.g., inverse propensity scoring), and offline metrics (AUC, NDCG). Plan online evaluation via A/B tests measuring engagement and user satisfaction.
Discuss deployment, monitoring for model drift, and continuous improvement through feedback loops and experimentation.
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