prediction_algorithms packageΒΆ

The prediction_algorithms package includes the prediction algorithms available for recommendation.

The available prediction algorithms are:

random_pred.NormalPredictor Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal.
baseline_only.BaselineOnly Algorithm predicting the baseline estimate for given user and item.
knns.KNNBasic A basic collaborative filtering algorithm.
knns.KNNWithMeans A basic collaborative filtering algorithm, taking into account the mean ratings of each user.
knns.KNNWithZScore A basic collaborative filtering algorithm, taking into account
knns.KNNBaseline A basic collaborative filtering algorithm taking into account a baseline rating.
matrix_factorization.SVD The famous SVD algorithm, as popularized by Simon Funk during the Netflix Prize.When baselines are not used, this is equivalent to Probabilistic Matrix Factorization [salakhutdinov2008a] (see note below)..
matrix_factorization.SVDpp The SVD++ algorithm, an extension of SVD taking into account implicit ratings.
matrix_factorization.NMF A collaborative filtering algorithm based on Non-negative Matrix Factorization.
slope_one.SlopeOne A simple yet accurate collaborative filtering algorithm.
co_clustering.CoClustering A collaborative filtering algorithm based on co-clustering.

You may want to check the notation standards before diving into the formulas.