Recommender Systems
Matrix factorisation, two-tower retrieval, ranking objectives, feedback loops and cold start.
12concepts
152flashcards
92minutes of reading
- 01 Cold Start and Content-Based Hybrids Why a new user or item is invisible to collaborative filtering, how hybrid models build representations from metadata instead of interaction history, and the exploration cost that no content feature removes.
- 02 Matrix Factorisation and Implicit Feedback Learning low-rank user and item vectors from a sparse interaction matrix, and why the shift from ratings to clicks changes the loss, the negatives and the meaning of the output.
- 03 Semantic IDs for Recommendation Why recommenders hash item identifiers into meaningless buckets, how quantising a content embedding into a short tuple of discrete codes replaces that hash with an address that carries similarity, and what the second training stage costs you.