I'm having a hard time understanding how the model learns the users and items biases and I'd loooove to have some guidance! haha
I do understand the fact that the bias term for user u is given by the sum of the features' biases, as Kula explains in his paper, however, I can't seem to understand how the model calculates the features' biases.
In other words, I know that when creating the FM, the model represents users (and items) as its' features latent vectors, and then users' biases originate from the sum of the features biases of users features. But how the model calculates these features biases? What are those?
I'm having a hard time understanding how the model learns the users and items biases and I'd loooove to have some guidance! haha
I do understand the fact that the bias term for user u is given by the sum of the features' biases, as Kula explains in his paper, however, I can't seem to understand how the model calculates the features' biases.
In other words, I know that when creating the FM, the model represents users (and items) as its' features latent vectors, and then users' biases originate from the sum of the features biases of users features. But how the model calculates these features biases? What are those?
love, Bec.