Date: 03/05/17
- Added FAQ in the doc
- Added the possibility to retrieve the k nearest neighbors of a user or an item.
- Changed the dumping process a bit (see API changes). Plus, dumps can now be loaded.
- Added possibility to build a testset from the ratings of a training set
- Added inner-to-raw id conversion in the Trainset class
- The r_ui parameter of the predict() method is now optional
- Fixed verbosity of the evaluate function
- Corrected prediction when only user (or only item) is unknown in SVD and NMF algorithms. Thanks to kenoung!
- Corrected factor vectors initialization of SVD algorithms. Thanks to adideshp!
- The dump() method now dumps a list of predition (optional) and an algorithm (optional as well). The algorithm is now a real algorithm object. The trainset is not dumped anymore as it is already part of the algorithm anyway.
- The dump() method is now part of the dump namespace, and not the global namespace (so it is accessed by surprise.dump.dump)
Date: 04/01/17
Just a minor change so that README.md is converted to rst for better rendering on PyPI.
Date: 02/01/17
- Added the GridSearch feature, by Maher
- Added a 'clip' option to the predict() method
- Added NMF algorithm
- Added entry point for better command line usage.
- Added CoClustering algorithm.
- Added SlopeOne algorithm.
- Added Probabilistic Matrix Factorization as an option SVD
- Cythonized Baseline Computation
- Surprise is now a scikit!
- Changed license to BSD
- Six is now a dependency
Date: 22/11/16
- Changed name from recsys to surprise
- Improved printing of accuracy measures.
- Added version number.
- Rewrote the the main.py
Date: 15/11/16
- Added notebooks for comparing and evaluating algorithm performances
- Better use of setup.py
- Added a min_support parameter to the similarity measures.
- Added a min_k parameter to the KNN algorithms.
- The similarity matrix and baselines are now returned.
- You can now train on a whole training set without test set.
- The estimate method can return a tuple with prediction details.
- Added SVD and SVD++ algorithms.
- Removed all the x/y vs user/item stuff. That was useless for most algorithms.
- Removed the @property decorator for many iterators.
- It's now up to the algorithms to decide if they can or cannot make a prediction.
Date: 25/10/16
- Added support for Python 2