Added

• Cython back-end for _fit(), _predict(), _recommend() - the Cython _fit() function is 5X-10X faster than the original Numba version, and predict()/recommend() are about the same speed.

Changed

• split regularization into two parameters: alpha to control the L2 regularization for user/item indicators and beta to control the regularization for user-features/item-features. In testing user-features/item-features tended to have exploding gradients/overwhelm utility scores unless more strongly regularized, especially with fairly dense side features. Typically beta should be set fairly high (e.g. 0.1) to avoid numerical instability.