API Reference#
Welcome to the API Reference. This section contains the documentation of the
functions and classes of the cornac package.
API Reference
- Data
- Models
- Recommender (Generic Class)
- Comparative Aspects and Opinions Ranking for Recommendation Explanations (Companion)
- Disentangled Multimodal Representation Learning for Recommendation (DMRL)
- Bilateral VAE for Collaborative Filtering (BiVAECF)
- Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)
- Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)
- Adversarial Training Towards Robust Multimedia Recommender System (AMR)
- Hybrid neural recommendation with joint deep representation learning of ratings and reviews (HRDR)
- Hypergraphs with Attention on Reviews for Explainable Recommendation
- Simplifying and Powering Graph Convolution Network for Recommendation (LightGCN)
- New Variational Autoencoder for Top-N Recommendations with Implicit Feedback (RecVAE)
- Predicting Temporal Sets with Deep Neural Networks (DNNTSP)
- Recency Aware Collaborative Filtering for Next Basket Recommendation (UPCF)
- Temporal-Item-Frequency-based User-KNN (TIFUKNN)
- Correlation-Sensitive Next-Basket Recommendation (Beacon)
- Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)
- Neural Graph Collaborative Filtering (NGCF)
- Collaborative Context Poisson Factorization (C2PF)
- Graph Convolutional Matrix Completion (GCMC)
- Multi-Task Explainable Recommendation (MTER)
- Neural Attention Rating Regression with Review-level Explanations (NARRE)
- Probabilistic Collaborative Representation Learning (PCRL)
- VAE for Collaborative Filtering (VAECF)
- Collaborative Variational Autoencoder (CVAE)
- Conditional VAE for Collaborative Filtering (CVAECF)
- Generalized Matrix Factorization (GMF)
- Indexable Bayesian Personalized Ranking (IBPR)
- Matrix Co-Factorization (MCF)
- Multi-Layer Perceptron (MLP)
- Neural Matrix Factorization (NeuMF/NCF)
- Online Indexable Bayesian Personalized Ranking (OIBPR)
- Visual Matrix Factorization (VMF)
- Collaborative Deep Ranking (CDR)
- Collaborative Ordinal Embedding (COE)
- Convolutional Matrix Factorization (ConvMF)
- Spherical k-means (Skmeans)
- Visual Bayesian Personalized Ranking (VBPR)
- Collaborative Deep Learning (CDL)
- Hierarchical Poisson Factorization (HPF)
- TriRank: Review-aware Explainable Recommendation by Modeling Aspects (TriRank)
- Explicit Factor Model (EFM)
- Social Bayesian Personalized Ranking (SBPR)
- Hidden Factors and Hidden Topics (HFT)
- Weighted Bayesian Personalized Ranking (WBPR)
- Collaborative Topic Regression (CTR)
- Baseline Only
- Bayesian Personalized Ranking (BPR)
- Factorization Machines (FM)
- Global Average (GlobalAvg)
- Item K-Nearest-Neighbors (ItemKNN)
- Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM)
- Matrix Factorization (MF)
- Maximum Margin Matrix Factorization (MMMF)
- Most Popular (MostPop)
- Non-negative Matrix Factorization (NMF)
- Probabilitic Matrix Factorization (PMF)
- Session Popular (SPop)
- Session-based Recommendations with Recurrent Neural Networks (GRU4Rec)
- Singular Value Decomposition (SVD)
- Social Recommendation using PMF (SoRec)
- User K-Nearest-Neighbors (UserKNN)
- Weighted Matrix Factorization (WMF)
- Metrics
- Evaluation Methods
- Experiment
- Built-in Datasets
- Hyper-parameter Tuning