first_example.py - Your very first example with Cornac.
pmf_ratio.py - Splitting data into train/val/test sets based on provided sizes (RatioSplit).
given_data.py - Evaluate the models with your own data splits.
vbpr_tradesy.py - Image features associate with items/users.
c2pf_example.py - Items/users networks as graph modules.
conv_mf_example.py - Text data associate with items/users.
bpr_netflix.py - Example to run Bayesian Personalized Ranking (BPR) with Netflix dataset.
biased_mf.py - Matrix Factorization (MF) with biases.
pmf_ratio.py - Probabilistic Matrix Factorization (PMF) with RatioSplit.
ibpr_example.py - Example to run Indexable Bayesian Personalized Ranking.
vbpr_tradesy.py - Visual Bayesian Personalized Ranking (VBPR) with Tradesy dataset.
c2pf_example.py - Collaborative Context Poisson Factorization (C2PF) with Amazon Office dataset.
conv_mf_example.py - Convolutional Matrix Factorization (ConvMF) with MovieLens dataset.
pcrl_example.py - Probabilistic Collaborative Representation Learning (PCRL) Amazon Office dataset.
ctr_example_citeulike.py - Collaborative Topic Modelling (CTR) with CiteULike dataset.
cdl_example.py - Collaborative Deep Learning (CDL) with CiteULike dataset.
cdr_example.py - Collaborative Deep Ranking (CDR) with CiteULike dataset.
cf_example.py - Collaborative Filtering for Implicit Feedback Datasets (CF) CiteULike.
cvae_example.py - Collaborative Variational Autoencoder (CVAE) with CiteULike dataset.
mcf_office.py - Fit Matrix Co-Factorization (MCF) to the Amazon Office dataset.
sbpr_epinions.py - Social Bayesian Personalized Ranking (SBPR) with Epinions dataset.