I went straight to user intent and started talking about watch history, engagement signals, that kind of thing.
Start by clarifying the goal and constraints, then walk through a structured design process covering data, algorithms, and evaluation. Focus on how you would balance user engagement, diversity, and business metrics while iterating based on feedback.
Pro tip: Emphasize the importance of defining success metrics early and aligning them with both user value and business goals; this shows product thinking beyond just technical implementation.
Ask clarifying questions to understand the scope, such as what 'similar' means (content, style, topic), target users, and business objectives. Define success metrics like click-through rate, watch time, and user satisfaction.
Identify data sources: video metadata (title, tags, description), user interactions (views, likes, shares), and content features (visual, audio, text). Discuss how to extract and represent features for similarity computation.
Choose appropriate recommendation algorithms: content-based filtering (e.g., TF-IDF, embeddings), collaborative filtering (user-item interactions), or hybrid approaches. Consider deep learning models for video understanding and sequence modeling.
Outline the system components: data pipeline, feature store, model training, serving layer, and feedback loop. Address scalability, latency, and real-time updates for millions of users and videos.
Define offline and online evaluation metrics (precision@k, recall, A/B testing). Discuss how to handle cold start, diversity, and avoid filter bubbles. Plan for continuous improvement based on user feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.