CS 451 Quiz 24
Recommender systems and collaborative filtering
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According to Andrew Ng, recommender systems *
Suppose user j has watched movie i and rated it 4 out of 5. Which of the following describes this scenario? *
To learn the movie ratings for user j, we can use *
Collaborative filtering is an algorithm for *
One way to implement collaborative filtering is to alternate estimating theta's from x's, and x's from theta's. Which of the following algorithms operates in a similar way? *
Instead of alternating the two estimation problems, we can estimate x's and theta's jointly in a single minimization problem. *
Why do we initialize x's and theta's to small random values? *
In collaborative filtering we don't need to include "bias" features x(0) = 1 and theta(0) = 1 *
Which of the following is NOT one of movie titles used as examples? *
I'd rather not get a free point on this quiz. *
HW 5 teams [not graded] *
Please enter one of "working with [your partners name]", "working alone", or "still looking for a partner"
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