Answer (Choose 2 answers)
5
Which of the following are true of collaborative filtering systems? Check all that apply.
A.. If you have a dataset of user ratings on some products, you can use these to predict one user's preferences on products he has not rated.
B. When using gradient descent to train a collaborative filtering system, it is okay to initialize all the parameters to zero.
C. For collaborative filtering, it is possible to use one of the advanced optimization algorithms (L-BFGS/conjugate gradient/etc.) to solve for all the parameters simultaneously.
D. For collaborative filtering, the optimization algorithm you should use is gradient descent. In particular, yo cannot use more advanced optimization algorithms (L-BFGS/conjugate gradient/etc.) for collaborative filteri since you have to solve for all the parameters simultaneously.
