Netflix Recommendation Engine

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Netflix's recommendation engine is a complex system that uses a variety of technologies and techniques to provide personalised recommendations to users based on their viewing history and preferences.

At a high level, the recommendation engine infrastructure consists of several key components:

  • Data collection: Netflix collects data on user interactions with the platform, including what shows and movies they watch, how long they watch for, and what they rate and review. This data is used to create personalised recommendations for each user.
  • Data storage: The data collected by Netflix is stored in a variety of data stores, including relational databases, NoSQL databases, and data warehouses. This allows for efficient querying and analysis of the data.
  • Data processing: Netflix uses a variety of tools and technologies to process and analyze the data collected from user interactions. This includes using machine learning algorithms to identify patterns and trends in the data, as well as natural language processing to understand and analyze user reviews and ratings.
  • Recommendation generation: Based on the data processed and analyzed, the recommendation engine generates personalized recommendations for each user. These recommendations are generated in real-time based on a user's current watching history and preferences.
  • Recommendation delivery: The recommendations generated by the recommendation engine are then delivered to users through various channels, including the Netflix website and app, email, and push notifications.

Overall, the recommendation engine infrastructure at Netflix is designed to efficiently collect, process, and analyse large amounts of data in order to provide personalised recommendations to users in real-time.

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