Spent way too long on features and modeling.
Start by clarifying the problem scope and requirements, then outline a high-level architecture that covers data collection, candidate generation, ranking, and evaluation. Emphasize the unique aspects of Roblox (user-generated content, social graph, real-time interactions) and discuss trade-offs between different approaches.
Pro tip: Demonstrate awareness of the cold-start problem for new games and users, and propose solutions like content-based filtering or leveraging social signals. Also, highlight the importance of online metrics (e.g., engagement, retention) and how to balance exploration and exploitation.
Ask questions to understand the platform's scale, user base, and business objectives. Define what 'surfacing games' means (e.g., homepage recommendations, search, notifications) and key metrics (e.g., DAU, session time, game plays).
Identify data sources: user interactions (plays, likes, favorites), game metadata (genre, tags, creator), social graph (friends, groups), and contextual data (time, device). Discuss feature engineering for users and games.
Propose a multi-stage system: candidate generation (e.g., collaborative filtering, content-based, trending) followed by ranking (e.g., deep learning model with features). Consider real-time vs batch processing.
Define offline metrics (e.g., precision@k, NDCG) and online metrics (e.g., CTR, engagement, retention). Discuss A/B testing and how to handle feedback loops.
Discuss trade-offs: model complexity vs latency, personalization vs diversity, exploration vs exploitation. Address scalability challenges (e.g., millions of users and games) and potential solutions (e.g., distributed training, caching).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.