Start by clarifying the problem scope and success metrics, then outline a two-stage recommendation system (candidate generation and ranking) that leverages user profile, activity, and course content. Emphasize how the system aligns with LinkedIn's goal of helping users succeed in job searches or current roles, and discuss trade-offs and evaluation.
Pro tip: Tie your design to LinkedIn's unique data (e.g., skills, job postings, endorsements) and business objectives—show how recommendations can drive both user success and platform engagement. Also, mention cold-start strategies and feedback loops for continuous improvement.
Ask questions to understand the scope: target users, course catalog size, data availability, and business goals. Define success metrics such as click-through rate, course completion, and job placement rate.
Identify relevant data sources: user profiles (skills, experience, goals), activity (searches, views, completions), course metadata (skills taught, difficulty, duration), and job market trends. Design features that capture user needs and course relevance.
Propose a two-stage system: candidate generation (e.g., collaborative filtering, content-based, or embedding-based retrieval) to narrow down thousands of courses to hundreds, then a ranking model (e.g., gradient boosted trees or neural network) to score and order them.
Define offline metrics (precision@k, NDCG) and online A/B testing. Plan for feedback loops: use implicit feedback (clicks, completions) to retrain models and improve recommendations over time.
Discuss serving infrastructure: batch vs. real-time, latency requirements, and scalability. Consider cold-start for new users/courses and how to handle dynamic job market trends.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.