Editing Recommendation Systems (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Recommendation systems solve the '''information overload''' problem: given millions of items and millions of users, what should each user see? The key insight is that users can be represented by their interaction history, and items can be represented by their features and interaction patterns.

'''Collaborative filtering''' rests on the assumption that users who agreed in the past tend to agree in the future. User-based CF: find similar users, recommend what they liked. Item-based CF: find items similar to what you've liked before. Matrix factorization learns latent user and item embeddings that capture these similarity patterns efficiently.

'''Neural recommenders''' have replaced classical CF for most large-scale systems. Two-tower models learn separate user and item representations; recommendation = nearest neighbors in the shared embedding space. Deep learning enables incorporating side features (user demographics, item attributes, context) that pure CF ignores.

'''The industrial pipeline''': Production recommenders have multiple stages:
1. '''Candidate generation''' (retrieval): Given user context, quickly retrieve N=1000 candidate items from millions. Uses efficient ANN search over learned embeddings.
2. '''Ranking''': A more complex model re-ranks the N candidates using richer features, producing the top-K displayed items.
3. '''Re-ranking''': Apply business rules, diversity constraints, and policy filters to the final list.

'''Beyond CTR''': Optimizing purely for CTR leads to clickbait and engagement traps. Production recommenders optimize multi-objective rewards: engagement + satisfaction + diversity + session length + business metrics.
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