Editing Recommendation Systems (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Collaborative filtering''' — Recommends items based on the preferences of similar users or by finding items similar to those a user has liked.
* '''Content-based filtering''' — Recommends items based on their attributes and the user's preference history (not other users).
* '''Hybrid recommender''' — Combines collaborative and content-based approaches to leverage strengths of both.
* '''User-item matrix''' — A matrix where rows are users, columns are items, and values are ratings or interaction signals; typically very sparse.
* '''Matrix factorization''' — Decomposing the user-item matrix into lower-dimensional user and item embedding matrices (SVD, ALS).
* '''Implicit feedback''' — Interaction signals without explicit ratings: clicks, views, time spent, purchases. Most real-world data is implicit.
* '''Explicit feedback''' — Direct ratings (1-5 stars); less common in practice but higher signal.
* '''Cold start problem''' — The difficulty of making recommendations for new users or new items with little or no interaction history.
* '''Long tail''' — The vast number of niche items with few interactions; recommenders must balance popular items with relevant niche ones.
* '''Click-through rate (CTR)''' — The fraction of recommended items that users click; a primary metric for recommendation quality.
* '''Two-tower model''' — A neural architecture with separate encoders for users and items that are then combined (common in industrial recommenders).
* '''Approximate Nearest Neighbor (ANN)''' — Fast similarity search in embedding space used to retrieve candidate items at scale (FAISS, ScaNN).
* '''Explore-exploit tradeoff''' — Balancing recommending items predicted to be liked (exploit) vs. items with uncertain engagement potential (explore).
* '''Diversity''' — Ensuring recommended items are not all from the same narrow category; improves user satisfaction and avoids filter bubbles.
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