Alternating Least Squares
A Recommendation Model based off the algorithms described in the paper ‘Collaborative Filtering for Implicit Feedback Datasets’ with performance optimizations described in ‘Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.’
This factory function switches between the cpu and gpu implementations found in implicit.cpu.als.AlternatingLeastSquares and implicit.gpu.als.AlternatingLeastSquares depending on the use_gpu flag.
- factors (int, optional) – The number of latent factors to compute
- regularization (float, optional) – The regularization factor to use
- dtype (data-type, optional) – Specifies whether to generate 64 bit or 32 bit floating point factors
- use_native (bool, optional) – Use native extensions to speed up model fitting
- use_cg (bool, optional) – Use a faster Conjugate Gradient solver to calculate factors
- use_gpu (bool, optional) – Fit on the GPU if available, default is to run on GPU only if available
- iterations (int, optional) – The number of ALS iterations to use when fitting data
- calculate_training_loss (bool, optional) – Whether to log out the training loss at each iteration
- num_threads (int, optional) – The number of threads to use for fitting the model. This only applies for the native extensions. Specifying 0 means to default to the number of cores on the machine.
- random_state (int, RandomState or None, optional) – The random state for seeding the initial item and user factors. Default is None.