I started with candidate generation and ranking, which felt natural, but the interviewer kept pulling me back to feature extraction from job postings.
Start by clarifying the problem scope and requirements, then outline a high-level system architecture that includes data collection, feature engineering, model training, and serving. Focus on how to leverage job application/click signals to generate course recommendations, and discuss trade-offs between different modeling approaches and evaluation metrics.
Pro tip: Emphasize the importance of cold-start and diversity in recommendations, and propose a hybrid approach that combines content-based and collaborative filtering to handle sparse signals. Also, mention how you would measure business impact beyond click-through rate, such as course completion and skill acquisition.
Ask questions to understand the scale, latency requirements, and success metrics. Clarify what data is available (e.g., job applications, clicks, course metadata) and whether real-time or batch recommendations are needed.
Identify key data sources: user profiles, job postings, course content, and interaction logs. Engineer features such as job title embeddings, course topic vectors, and user-course affinity scores.
Propose a hybrid recommendation model: use content-based filtering to map job requirements to course skills, and collaborative filtering to leverage similar users' behavior. Consider matrix factorization or deep learning models for scalability.
Design a scalable architecture with offline training and online serving. Use a two-stage approach: candidate generation (e.g., via ANN) and ranking (e.g., with a deep model). Ensure low-latency responses and handle cold-start users.
Define offline metrics (precision@k, recall) and online metrics (CTR, completion rate). Plan A/B tests to measure impact on user engagement and learning outcomes. Iterate based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.