Name |
Description |
AlbumCounter |
Predict item that has the album that a user accessed most |
ArtistCounter |
Predict item that has the artist that a user accessed most |
AttributeCounter |
Simple recommender that counts attributes |
BPRMF |
Matrix factorization model for item prediction (ranking) optimized for BPR |
BPRMF_KDD |
BPRMF with frequency-adjusted sampling, prototype for KDD Cup 2011 |
BPR_Linear |
Linear model optimized for BPR |
BPR_SMF_KDD |
BPRMF with frequency-adjusted sampling and shared factors |
BalancedLogisticRegressionMatrixFactorization |
Matrix factorization model optimized for balanced logistic regression. |
FilterBPRMF |
|
GenreCounter |
Predict item that has the genres that a user accessed most |
ItemAttributeSVM |
Content-based filtering using one support-vector machine (SVM) per user |
ItemKNN |
Unweighted k-nearest neighbor item-based collaborative filtering using cosine similarity |
ItemRecommender |
Abstract item recommender class that loads the training data into memory |
KNN |
Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model |
MF |
Abstract class for matrix factorization based item predictors |
MostPopular |
Most-popular item recommender |
Prediction |
Class that contains static methods for item prediction |
SoftMarginRankingMF |
Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF). |
TransductiveBPRMF |
BPR variant that takes into account some test data for training |
TransductiveBPRMF_KDD |
Transductive BPRMF with frequency-adjusted sampling, prototype for KDD Cup 2011 |
TypedBPRMF |
BPRMF with frequency-adjusted sampling and shared factors |
UserKNN |
k-nearest neighbor user-based collaborative filtering |
WRMF |
Weighted matrix factorization method proposed by Hu et al. and Pan et al. |
WRMF_KDD |
Weighted matrix factorization method proposed by Hu et al. and Pan et al.; adapted to KDD Cup 2011 |
WeightedItemKNN |
Weighted k-nearest neighbor item-based collaborative filtering using cosine similarity |
WeightedUserKNN |
Weighted k-nearest neighbor user-based collaborative filtering using cosine-similarity |
Zero |
Constant item recommender for use as experimental baseline. Always predicts a score of zero |