3 ms·
So SigOpt was tuning rank (number of latent factors), number of iterations to run the algorithm (in my experience alternating least squares generally converges
by a1k0n 10y ago
So SigOpt was tuning rank (number of latent factors), number of iterations to run the algorithm (in my experience alternating least squares generally converges within 10-20 iterations, but there'd be no downside to running it longer unless it's overfitting), and the regularization strength.
What optimal parameters did it find for these?
- Zephyr314 10y agoGreat question. As you point out, all of these parameters (rank, num iterations and the actual reg. term) can in some way contribute to the regularization of the reconstruction of the ratings matrix. We thought it would be interesting to include all of them and here are the optimal parameters SigOpt found for this experiment: rank = 36 numIter = 30 log_lambda = -2.90405347693