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.

../_images/example_latent_factor_models1.png