* Highlights
* New Features
* Improvements
* Bug Fixes
Highlights
The RecBole v0.2.0 release includes a number of new features, model efficiency improvements and bug fixes. A few of the highlights include:
1. We add 12 new models into RecBole, including several **non-sampling models** and an external algorithm lib model: **XGBoost**.
2. **Case study** is added to RecBole, which is helpful for users to analyze the model result (e.g: give an item ID and a user ID and get the score and ranking position of the item).
3. We improve the efficiency of data loading and negative sampling.
4. We now support the full ranking evaluation for context-aware recommendation models.
New Features
* Add 12 new models:
* Sequential recommendation(6) : RepeatNet(\584), Fossil(\580), HGN(575), SHAN(573), NPE(573),HRM(573)
* General recommendation(5) : CDAE(642), MacridVAE(637), MultiVAE(626), MultiDAE(626), LINE(\591)
* External algorithm lib model(1) : XGBoost(\557)
* ``training_neg_sample_num`` of pairwise_loss model now can be greater than `1` (533).
* We add `training_neg_sample_distribution` in config setting to choose the negative sampling strategy during training (534).
* We add `benchmark_filename` in config setting to load pre-split dataset (\596).
* Progress bar is added for training and evaluating (\618).
* We add `loss_decimal_place` and `metric_decimal_place` in config setting to control decimal place of loss and metric results separately (625).
* We add `GAUC` metric into evaluation (reference: [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/abs/1706.06978), KDD 2018) (#572).
* We add `unused_col` in config setting to drop the columns only used in data preparation but not used in model (559).
Improvements
* We support the ranking evaluation for context-aware recommendation models (503).
* We improve the efficiency of data loading and negative sampling (559).
* We remove the `pre_neg_sampling` in Dataloader, which is helpless to model training (559).
* We improve the underlying data structure of RecBole, which can promote efficiency of data processing (559).
* We refactor the evaluation code (572) and reformat the mode code (647).
Bug Fixes
Model
* Fix a bug in NeuMF model: this bug may cause `dropout_ratio` disable (629).
* Fix a bug in NGCF and GCMC model: now the sparse dropout is disable during evaluation in NGCF and GCMC (601).
* Fix a bug in DCN model: this bug may cause crash when running on CPU (633).
* Fix a bug in BERT4Rec model: this bug may cause crash when running on CPU (556).
Trainer
* Fix a bug in the `Trainer._generate_train_loss_output()`: this bug may cause the training log is missing (559).
Data
* Fix a bug in the sampler: this bug may cause runtime error (559)