Cornac

New models

- Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF) (215)
- Generalized Matrix Factorization (GMF) (216)
- Multi-Layer Perceptron (MLP) (217)

New features and improvements

- Update `VAECF` model (213)
- Add `num_zeros` argument into `Dataset.uir_iter()` to support negative sampling (214)
- Add `val_set` into `fit()` function for model selection (219)
- Support `early stopping` in `Recommender` (220)
- Fix bug in `read_text()` function of reader (221)
- Unify `TrainSet` and `TestSet` into single object `Dataset` (222)
- Optimize `evaluate()` function in `BaseMethod` (223)
- `Dataset` checks duplicate observations independently (224)

New models

- Baseline Only (208)
- Singular Value Decomposition (SVD) (207)

New features and improvements

- Remove `shuffle` option in `RatioSplit` (209)
- Fix bug of binarizing rating values in `Reader` (211)
- Add `UITup` data format to `Reader` (212)

New models

- Non-negative Matrix Factorization (NMF) (204)

New features and improvements

- Tutorial on cross-modality from Text to Graph (199)
- Tutorial on cross-modality from Text to Image (195)
- Input data to `Modality` left unchanged (205)

New features and improvements

- Add random seed to `BaseMethod` used for all evaluation methods (198)
- Add random seed to `MatrixTrainSet` for reproducing data sampling (202)
- Update BPR, SBPR, and MF models to use single-thread when the random seed is specified (200, 201)

New models

- Hidden Factors and Hidden Topics (HFT) (196)

New features and improvements

- Amazon Clothing dataset (197)
- CountVectorizer updated with vocab conditions (193)

New features and improvements

- Rename `Module` to `Modality`
- Add a tutorial for adding a new model into Cornac
- Reorder the list of models by year then alphabetically
- Update description, tagline
- Improve documentation