Recommender Systems

The AI behind personalization — deciding what to show, suggest, or rank for each user. The quiet workhorse of the internet.

Recommender Systems is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  U[/User/] --> CAND[Candidate generation] --> RANK[Ranking] --> RR[Re-rank / filter] --> FEED[/Personalized feed/]
  ITEMS[(Item catalog)] -. embeddings .-> CAND

Key topics

• Collaborative filtering

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  Recommend from what similar users liked — matrix factorization and neighborhood methods.

• Content-based filtering

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  Match items to a user from item features and past preferences.

• Learning to rank

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  Order candidates by predicted relevance — pointwise, pairwise, and listwise ranking.

• Two-tower retrieval

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  Embed users and items separately, then retrieve nearest neighbors at scale.

• Cold start & the long tail

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  Recommending for brand-new users and items with little data.

• Feedback loops & bias

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  How recommendations shape behavior — popularity bias, filter bubbles, and evaluation pitfalls.

Related areas

Machine Learning · Information Retrieval & Search · Data & MLOps

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