Enhancing Recommender Systems for Large-Scale Data

Requirements Clarification & Assessment

1. Understanding the Scope:

   • Dataset Size: The Netflix dataset includes millions of movies and users, necessitating scalability and efficiency.
   • User-Movie Interaction: Must capture user preferences and movie characteristics effectively.
   • Real-time Recommendations: System should provide recommendations in real-time as users interact with the platform.

2. Functional Requirements:

   • Accuracy: High recommendation accuracy to ensure user satisfaction.
   • Scalability: Ability to handle an increasing number of users and movies.
   • Latency: Minimal delay in generating recommendations.

3. Non-Functional Requirements:

   • Reliability: System should be robust and fault-tolerant.
   • Maintainability: Easy to update models with new data.
   • Security: Ensure user data privacy and protection.

4. Constraints:

   • Computational Resources: Limited by available hardware and infrastructure.
   • Time Constraints: Quick model deployment and iteration cycles.

5. Stakeholder Expectations:

   • Business Goals: Increase user engagement and retention.
   • Technical Team: Feasibility of implementation and maintenance.