The one-input constraint is what makes this interesting and also where I got a bit turned around.
Start by clarifying the product goal and the single input (e.g., a mood, genre, or artist), then propose a scalable algorithm that balances personalization, diversity, and freshness. Structure your answer around user needs, data signals, and system constraints, and discuss how to evaluate success.
Pro tip: Emphasize that the algorithm must handle cold start and avoid repetition, and suggest a hybrid approach combining collaborative filtering with content-based filtering. Also, mention the importance of real-time feedback loops to adapt to user behavior.
Ask clarifying questions to understand what the single input represents (e.g., a seed song, genre, mood) and what the product objective is (e.g., maximize engagement, discovery, or retention).
Propose metrics such as user engagement (skip rate, listen time), diversity of recommendations, and freshness to evaluate the algorithm's performance.
Describe a hybrid recommendation system that uses the input to generate candidate songs via content-based filtering, then ranks them using collaborative filtering and contextual signals (e.g., time of day, user history).
Explain how to handle a database of over a million tracks efficiently, using techniques like approximate nearest neighbors, precomputed embeddings, and caching.
Propose A/B testing and offline evaluation to refine the algorithm, and mention the need for real-time feedback to adapt to user preferences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.