Section outline

  • ·         Overview: Recommendation systems help suggest products, movies, or content to users. This lesson frames recommenders through the lens of the three main machine learning paradigms: supervised learning (predicting ratings or preferences), unsupervised learning (finding similarities, e.g., collaborative filtering), and reinforcement learning (learning by trial and reward). Students will understand how each approach contributes to making smart recommendations.

    • Micro-Topic 12.1: The Recommendation Problem – Goal: Introduce what recommendation systems do and the challenges involved.

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    • Micro-Topic 12.2: Supervised Learning for Recommendations – Goal: Explain how recommendations can be treated as a prediction (supervised) problem.

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    • Micro-Topic 12.3: Unsupervised Learning for Recommendations (Collaborative Filtering) – Goal: Show how finding patterns without explicit labels can drive recommendations, e.g., via user-user or item-item similarity.

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    • Micro-Topic 12.4: Reinforcement Learning for Recommendations – Goal: Explain how recommendation can be viewed as a sequential decision problem, optimizing long-term user satisfaction.

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    • Micro-Topic 12.5: Combining Approaches – Building Better Recommenders – Goal: Discuss how real systems often hybridize methods and consider practical aspects.

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