Recommender Systems
3concepts
23flashcards
22minutes of reading
- 01 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.
- 02 Feedback Loops and Filter Bubbles A recommender trained on data it generated is optimising against its own past choices, which narrows what users see and makes offline evaluation systematically agree with the incumbent.
- 03 Two-Tower Retrieval and Candidate Generation Splitting the model so that item representations can be precomputed and searched with approximate nearest neighbours, which is what makes recommending from a hundred-million-item catalogue possible at all.