- Fix bug of list multiplication in `ranking_eval()` function (236)
1.1.0
New models
- Explicit Factor Model (EFM) (181)
New features and improvements
- Rename model CF to WMF (226) - Remove usage of old Dataset from IBPR model (227) - Use Cornac's mini-batch iterator in PCRL (229) - Support early stopping for GMF, MLP, and NeuMF (233)
- Support item negative sampling based on popularity (228) - Remove data_utils.py (230) - Add Amazon Toys and Games dataset (179) - Add Sentiment Modality (180) - Add binary argument into uir_iter() (231 - Move rating and ranking eval functions out for reuse (232)
- 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)
0.3.5
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)
0.3.4
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)
0.3.3
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)