Trust-based matrix factorization, which extends SVD++ with trust information.



  • Data

    Data set.

  • Preprocessor

    Preprocessed data.

  • Trust information

    Trust information. The weights of the connections can be integer or float (binary relations can represented by 0 or 1).


  • 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.

  • W

    Latent features of the trust information.


TrustSVD widget uses a biased regularized algorithm which makes use of implicit feedback information and trust information to factorize a matrix into four low rank matrices as it’s explained in Guibing Guo, Jie Zhang, Neil Yorke-Smith, TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings


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.