I jumped straight into collaborative filtering and probably spent too long there before they nudged me toward cold start.
Start by clarifying the problem scope and requirements, then outline a high-level system design covering data collection, feature engineering, model selection, and serving. Emphasize the trade-offs between different approaches and how you would evaluate and iterate on the system.
Pro tip: Focus on the unique aspects of Confluence data, such as hierarchical page structures and user collaboration patterns, and discuss how to handle cold-start and scalability in an enterprise setting.
Ask questions to understand the goal: Is it to increase engagement, reduce search time, or surface relevant content? Determine scale, latency, and personalization needs.
Identify data sources: user interactions (views, edits, comments), page content (text, metadata), and organizational graph. Discuss feature engineering for user, page, and context.
Choose between collaborative filtering, content-based, or hybrid models. Consider deep learning for sequence modeling (e.g., RNNs, transformers) to capture user behavior over time.
Design offline training pipeline and online serving with low latency. Discuss candidate generation, ranking, and re-ranking, and how to handle updates and feedback loops.
Define offline metrics (precision@k, recall, NDCG) and online metrics (CTR, dwell time). Plan A/B testing and monitoring for model drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.