Cornac 0.3.0

New models

• Variational Autoencoder for Collaborative Filtering (VAECF)
• Collaborative Topic Modelling (CTR)

New features and improvements

• Update CVAE to use mini-batch gradient descent
• Remove stopwords from TextModule and CountVectorizer, only input stopwords directly to BaseTokenizer
• GraphModule can build KNN from input feature matrix
• Fix bug of partitioning in CrossValidation
• Swap both ids according to data in FeatureModule and TextModule

Cornac 0.2.1

New models

• Collaborative Variational Autoencoder (CVAE)

New features and improvements

• Update from_splits() function of BaseMethod to support multimodal data modules
• Data modules build() functions return self

Beta Release

New models

• Convolutional Matrix Factorization (ConvMF)
• Collaborative Deep Ranking (CDR)
• Visual Matrix Factorization (VMF)
• Matrix Co-Factorization (MCF)
• Social Bayesian Personalized Ranking (SBPR)
• Social Recommendation (SoRec)

New built-in datasets

• Netflix
• Tradesy
• Amazon Office
• CiteULike
• Epinions

New features and improvements

• Support multimodal recommenders with new data module including TextModule, ImageModule, and GraphModule in data.
• Reader support data filtering based on set of users/items and user/item threshold.
• Models can access to user_text, user_image, user_graph, item_text, item_image, and item_graph through MultimodalTrainSet which is input of the fit() function.

Credits

Thanks to our 4 contributors (alphabetical) whose commits are featured in this release:

@binhtrantt @jyguo @saghiles @tqtg

Alpha Release

New models

• Bayesian Personalized Ranking (BPR)
• Collaborative Context Poisson Factorization (C2PF)
• Collaborative Deep Learning (CDL)
• Collaborative Ordinal Embedding (COE)
• Hierarchical Poisson Factorization (HPF)
• Indexable Bayesian Personalized Ranking (IBPR)
• Online Indexable Bayesian Personalized Ranking (Online IBPR)
• Probabilistic Collaborative Representation Learning (PCRL)
• Probabilistic Matrix Factorization (PMF)
• Spherical K-means (SKM)
• Visual Bayesian Personalized Ranking (VBPR)

Credits

Thanks to our 5 contributors (alphabetical) whose commits are featured in this release:

@Andrew-DungLe @jyguo @llkute @saghiles @tqtg