Recommender Systems
Matrix factorisation, two-tower retrieval, ranking objectives, feedback loops and cold start.
3concepts
46flashcards
22minutes of reading
- 01 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.
- 02 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.