Glossary · Phase 3 · Machine learning
Cold start
A new item or user with no behavior yet, which collaborative filtering can't place; content features and exploration fill the gap.
Try it now
The same ratings, one row per rating. Group them by item, then switch between the mean and the count.
Learn it properly
Recommender systems · Shrunk averages, item-to-item collaborative filtering, matrix factorization, and cold start. About 35 minutes.
Other terms from the same lesson
- collaborative filtering: Recommending from behavior alone: items are similar when the same people rated or bought them alike.
- matrix factorization: Learning a short vector for every user and item so that their dot product predicts the user's rating of the item.
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