Predictive Search Algorithm Design

Requirements Clarification & Assessment

1. Understanding the Problem:

   • Objective: Develop a recommendation algorithm for predictive search functionality on Netflix. The algorithm should suggest relevant movie/show titles, genres, actors, or directors as the user types.
   • Constraints: The solution must be highly efficient and scalable to handle millions of users simultaneously.
   • Output: Suggestions should be personalized, relevant, and delivered in real-time.

2. Key Questions to Address:

   • Language Support: Should the algorithm support multiple languages, or is it limited to English?
   • Result Display: How many suggestions should be displayed? Is it a fixed number (e.g., top 5 or top 10)?
   • Regional Considerations: Should the algorithm consider regional popularity or global trends?
   • User Base: How many users will be using the system simultaneously, and what is the expected query volume?

3. User Profile Data:

   • Existing Data: What user data is available for personalization (watch history, likes, search history)?
   • Privacy Concerns: Are there any privacy constraints or data usage policies that need to be considered?

4. Performance Metrics:

   • Latency: What is the acceptable response time for delivering suggestions?
   • Scalability: How will the system handle growth in user base and content catalog?

5. Integration with Existing Systems:

   • Compatibility: How will the new algorithm integrate with Netflix's existing infrastructure and recommendation systems?