Wouldn't be a good idea to make this a multithread solution?
The rating prediction for the trainset/testset, significantly slows down the training process when the amount of ratings increase significantly. This is a modification that has little to none complexity to implement and influences the performance significantly.
Thats just a suggestion.
Thank you,
André
PS: a parameter would be a good idea. Either the multithreading is activated or not.
PS2: My solution currently does everything in multithreading then when I use LibFM it simply slows down and instead of using 32 cores uses 1 core. Unfortunately, I'm not familiarized enough with libfm source code to develop the modification.
Hi,
Wouldn't be a good idea to make this a multithread solution?
The rating prediction for the trainset/testset, significantly slows down the training process when the amount of ratings increase significantly. This is a modification that has little to none complexity to implement and influences the performance significantly.
Thats just a suggestion.
Thank you, André
PS: a parameter would be a good idea. Either the multithreading is activated or not. PS2: My solution currently does everything in multithreading then when I use LibFM it simply slows down and instead of using 32 cores uses 1 core. Unfortunately, I'm not familiarized enough with libfm source code to develop the modification.