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System Design Question

Design a Recommendation Engine for E-commerce

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  1. Functional Requirements:

    • User Personalization:
      • Provide personalized product recommendations based on user behavior, preferences, and purchase history.
    • Real-time Recommendations:
      • Generate recommendations in real-time as users browse the site.
    • Diverse Product Suggestions:
      • Ensure a variety of product categories are suggested to users to enhance discovery.
    • Contextual Recommendations:
      • Adapt recommendations based on the current context, such as time of day, season, or ongoing promotions.
    • Feedback Loop:
      • Allow users to provide feedback on recommendations to improve future suggestions.
    • Cross-Platform Consistency:
      • Ensure recommendations are consistent across different platforms (web, mobile, etc.).
    • Scalability:
      • Handle a large number of users and products efficiently.
  2. Non-Functional Requirements:

    • Performance:
      • Recommendations should be generated with minimal latency to ensure a seamless user experience.
    • Scalability:
      • The system should scale to accommodate growing user bases and expanding product catalogs.
    • Reliability:
      • Ensure high availability and fault tolerance to maintain consistent service.
    • Security:
      • Protect user data and ensure privacy in recommendation processes.
    • Maintainability:
      • The system should be easy to update and maintain, allowing for quick integration of new algorithms or data sources.
    • Usability:
      • Recommendations should be easy to understand and interact with for users.
    • Compliance:
      • Adhere to relevant data protection regulations and industry standards.
  3. Constraints:

    • Data Privacy:
      • Ensure compliance with data protection laws such as GDPR or CCPA.
    • Resource Limitations:
      • Operate within the constraints of available computational resources and budget.
    • Integration:
      • Seamlessly integrate with existing e-commerce platforms and infrastructure.
    • Algorithmic Bias:
      • Minimize bias in recommendations to ensure fairness and inclusivity.
    • Data Freshness:
      • Maintain up-to-date data to ensure recommendations are relevant and timely.

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