Avg Release Cycle
- ➕ 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.
- ➕ Added support for Python 2