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Recommender Systems: The Two-Stage Machine

No model ranks the whole catalogue: a cheap retrieval stage cuts millions of items to hundreds, an expensive ranker orders those hundreds, and the trap nobody warns you about is that the model trains on clicks it caused. This path builds the architecture, the two-tower geometry of taste, the objective functions that encode what a product values, and the feedback loops and exploration budgets that decide what the system becomes.

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Lessons, in order

  1. 1
    AI
    The Two-Stage Machine: Why No Model Ranks the Whole Catalogue
    Start
  2. 2
    AI
    Two Towers: How Taste Becomes Geometry
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  3. 3
    AI
    Ranking and Objectives: What Should the Model Optimise?
    Start
  4. 4
    AI
    Feedback Loops: The Model Trains on Clicks It Caused
    Start