SVD++¶
Matrix factorization model which makes use of implicit feedback information.
Signals¶
Inputs:
Data
Data set.
Preprocessor
Preprocessed data.
Feedback information
Implicit feedback information. Optional, if None (default), it will be inferred from the ratings.
Outputs:
Learner
The learning algorithm with the supplied parameters.
Predictor
Trained recommender. Signal Predictor sends the output signal only if input Data is present.
P
Latent features of the users.
Q
Latent features of the items.
Y
Latent features of the implicit information.
Description¶
SVD++ widget uses a biased regularized algorithm which makes use of implicit feedback information to factorize a matrix into three low rank matrices as it’s explained in Y. Koren, Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
Example¶
Below is a simple workflow showing how to use both the Predictor and the Learner output. For the Predictor we input the prediction model into Predictions widget and view the results in Data Table. For Learner we can compare different learners in Test&Score widget.