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Lecture 09

In this lecture we discussed recommendation systems in general, and collaborative filtering in particular. With the Netflix Prize as a motivating example, we saw that simple memory-based methods (e.g., nearest-neighbors) are surprisingly effective in practice, although model-based methods (e.g., matrix factorization) often have a number of advantages in scalability and performance. See the slides for more details.