When building a personalisation feature that must work even for new users with no history.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Machine Learning & AI Engineering - Use case: Recommendation System Builder - Source task: - Build a recommendation system for {{describe_what_is_being_recommended_products_content_users_to_fol}}. Data available: {{describe_user_interaction_data_item_features_user_features}}. - Step 1: algorithm selection (collaborative filtering, content-based, hybrid : recommend and justify). - Step 2: implementation code for candidate generation and ranking stages. - Step 3: offline evaluation (MAP@K, NDCG, coverage, diversity). - Step 4: online evaluation design (CTR, conversion rate A/B test). - Step 5: cold start strategy for new users and new items. # Goal Algorithm recommendation, candidate generation and ranking code, offline and online evaluation design, and cold start strategy. # Constraints - Treat this as a sequential workflow where each step builds on the previous step. - Keep every step clearly labeled and easy to run separately if needed. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Algorithm recommendation, candidate generation and ranking code, offline and online evaluation design, and cold start strategy.
{{double-curly}} with your real context.When building a personalisation feature that must work even for new users with no history.
Two-stage retrieval (fast candidate generation + slower re-ranking) is the standard architecture for production recommendation systems at any meaningful scale.
Debug this problem systematically. Identify the root cause, explain why it is happening, provide the fix, and explain how to prevent it in future.
Design the high-level architecture for this system. Cover components, data flow, scaling strategy, and key design decisions.
Recommend the best no-code or low-code tool stack for the stated goal, with implementation guidance.
Design the complete analysis approach for the stated question. Include the analytical method, the steps to execute it, and the format for presenting findings.