Which machine learning technique is primarily used by major streaming platforms to power their film and TV show recommendation systems?

In today's competitive streaming landscape, recommendation algorithms are crucial for audience retention and content discovery. Major platforms invest heavily in machine learning systems that analyze viewing patterns and preferences to keep viewers engaged. This trivia question tests your knowledge about how these recommendation engines actually work behind the scenes in the entertainment industry.

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Collaborative filtering, which analyzes patterns across many users' viewing behaviors to find similarities and make predictions
Random forest algorithms, which classify content based solely on metadata like genre, director, and cast
Sentiment analysis, which recommends content based on reviews and social media reactions to similar titles
Reinforcement learning, which tests random recommendations and adjusts solely based on whether users complete watching the content