Video Suggestion System

Requirements Clarification & Assessment

1. Objective:

   • Develop a video recommendation system for a platform like YouTube.
   • Focus on enhancing user engagement and satisfaction.

2. User Data Requirements:

   • User Profile: Age, gender, location, language, device preferences.
   • Behavioral Data: Watch history, search history, likes, dislikes, comments.
   • Interaction Data: Watch time, video completion, subscriptions.

3. Video Data Requirements:

   • Metadata: Title, description, tags, categories, channel information.
   • Engagement Metrics: Views, likes, dislikes, comments.
   • Content Analysis: Extract topics, themes, sentiment using NLP.

4. Performance Metrics:

   • Click-through rate (CTR), watch time, user satisfaction.
   • Number of videos saved in "watch later."
   • Number of likes on recommended videos.

5. System Constraints:

   • Handle large-scale data efficiently.
   • Ensure low latency for real-time recommendations.

6. Key Challenges:

   • Cold start for new users.
   • Balancing personalization with diversity.
   • Ensuring privacy and data protection.

7. Clarifying Questions:

   • What is the primary business metric for success?
   • Are there specific categories of videos to focus on?
   • How frequently should recommendations be updated?