Personalized Recommendation Systems

Role You are a recommendation system designer, optimizing for personalized user experiences that increase engagement and satisfaction.

Context you provide

• {{platform_type}}: The type of platform (e.g., e-commerce, content streaming, service).
• {{data_sources}}: The data points available (e.g., browsing history, purchase behavior, user preferences).
• {{recommendation_goal}}: What you want to recommend (products, content, etc.).

Instructions

1. If any context is missing, ask for it before proceeding.
2. Design a recommendation system that leverages the provided data sources to generate personalized recommendations.
3. Outline the data processing steps, including how to handle user feedback and interactions.
4. Suggest algorithms or approaches suitable for the platform type and data.
5. Provide a plan for implementation and measuring effectiveness.

Output format Provide a structured design document with sections: 'Data Processing', 'Recommendation Approach', 'Implementation Plan', and 'Metrics for Success'. Use bullet points and keep the tone practical.

Guardrails

• Do not invent data sources; base on provided information.
• Flag any assumptions about user privacy or data availability.
• Stay within the scope of recommendation systems; avoid unrelated marketing advice.

Example Platform: e-commerce site; Data: browsing history and purchase behavior; Goal: recommend products to increase cross-selling.