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.
propensity_stratified_evaluation_example.py - Evaluate the models with Propensity Stratified Evaluation method.
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.
param_search.py - Hyper-parameter tuning with GridSearch and RandomSearch.
c2pf_example.py - Collaborative Context Poisson Factorization (C2PF) with Amazon Office dataset.
cvaecf_filmtrust.py - Fit and evaluate Conditional VAE (CVAECF) on the FilmTrust dataset.
gcmc_example.py - Graph Convolutional Matrix Completion (GCMC) example with MovieLens 100K dataset.
lightgcn_example.py - LightGCN example with CiteULike dataset.
mcf_office.py - Fit Matrix Co-Factorization (MCF) to the Amazon Office dataset.
ngcf_example.py - NGCF example with CiteULike dataset.
pcrl_example.py - Probabilistic Collaborative Representation Learning (PCRL) Amazon Office dataset.
sbpr_epinions.py - Social Bayesian Personalized Ranking (SBPR) with Epinions dataset.
sorec_filmtrust.py - Social Recommendation using PMF (Sorec) with FilmTrust dataset.
cdl_example.py - Collaborative Deep Learning (CDL) with CiteULike dataset.
cdr_example.py - Collaborative Deep Ranking (CDR) with CiteULike dataset.
conv_mf_example.py - Convolutional Matrix Factorization (ConvMF) with MovieLens dataset.
ctr_example_citeulike.py - Collaborative Topic Regression (CTR) with CiteULike dataset.
cvae_example.py - Collaborative Variational Autoencoder (CVAE) with CiteULike dataset.
dmrl_example.py - Disentangled Multimodal Representation Learning (DMRL) with citeulike dataset.
trirank_example.py - TriRank with Amazon Toy and Games dataset.
efm_example.py - Explicit Factor Model (EFM) with Amazon Toy and Games dataset.
hft_example.py - Hidden Factor Topic (HFT) with MovieLen 1m dataset.
lrppm_example.py - Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM) with Amazon Toy and Games dataset.
mter_example.py - Multi-Task Explainable Recommendation (MTER) with Amazon Toy and Games dataset.
causalrec_clothing.py - CausalRec with Clothing dataset.
dmrl_clothes_example.py - Disentangled Multimodal Representation Learning (DMRL) with Amazon clothing dataset.
vbpr_tradesy.py - Visual Bayesian Personalized Ranking (VBPR) with Tradesy dataset.
vmf_clothing.py - Visual Matrix Factorization (VMF) with Amazon Clothing dataset.
biased_mf.py - Matrix Factorization (MF) with biases.
bpr_netflix.py - Example to run Bayesian Personalized Ranking (BPR) with Netflix dataset.
ease_movielens.py - Embarrassingly Shallow Autoencoders (EASEᴿ) with MovieLens 1M dataset.
fm_example.py - Example to run Factorization Machines (FM) with MovieLens 100K dataset.
hpf_movielens.py - (Hierarchical) Poisson Factorization vs BPR on MovieLens data.
ibpr_example.py - Example to run Indexable Bayesian Personalized Ranking.
knn_movielens.py - Example to run Neighborhood-based models with MovieLens 100K dataset.
mmmf_exp.py - Maximum Margin Matrix Factorization (MMMF) with MovieLens 100K dataset.
ncf_example.py - Neural Collaborative Filtering (GMF, MLP, NeuMF) with Amazon Clothing dataset.
nmf_example.py - Non-negative Matrix Factorization (NMF) with RatioSplit.
pmf_ratio.py - Probabilistic Matrix Factorization (PMF) with RatioSplit.
recvae_example.py - New Variational Autoencoder for Top-N Recommendations with Implicit Feedback (RecVAE).
skm_movielens.py - SKMeans vs BPR on MovieLens data.
svd_example.py - Singular Value Decomposition (SVD) with MovieLens dataset.
vaecf_citeulike.py - Variational Autoencoder for Collaborative Filtering (VAECF) with CiteULike dataset.
wmf_example.py - Weighted Matrix Factorization with CiteULike dataset.
spop_yoochoose.py - Next-item recommendation based on item popularity.
gru4rec_yoochoose.py - Example of Session-based Recommendations with Recurrent Neural Networks (GRU4Rec).
gp_top_tafeng.py - Next-basket recommendation model that merely uses item top frequency.
dnntsp_tafeng.py - Predicting Temporal Sets with Deep Neural Networks (DNNTSP).
beacon_tafeng.py - Correlation-Sensitive Next-Basket Recommendation (Beacon).
tifuknn_tafeng.py - Example of Temporal-Item-Frequency-based User-KNN (TIFUKNN).
upcf_tafeng.py - Example of Recency Aware Collaborative Filtering for Next Basket Recommendation (UPCF).