See the GitHub milestone for full change list.
Fix inconsistency in both code and docs for recommend list sizes for top-N evaluation.
Fix user-user to correctly use
Improve performance and documentation
See the GitHub milestone for a summary of what’s happening!
loadmethods on algorithms have been removed. Just pickle fitted models to save their data. This is what SciKit does, we see no need to deviate.
The APIs and model structures for top-N recommendation is reworked to enable algorithms to produce recommendations more automatically. The
Recommenderinterfaces now take a
CandidateSelectorto determine default candidates, so client code does not need to compute candidates on their own. One effect of this is that the
batch.recommendfunction no longer requires a candidate selector, and there can be problems if you call
Recommender.adaptbefore fitting a model.
Top-N evaluation has been completely revamped to make it easier to correctly implement and run evaluation metrics. Batch recommend no longer attaches ratings to recommendations. See Top-N evaluation for details.
Batch recommend & predict functions now take
nprocsas a keyword-only argument.
Several bug fixes and testing improvements.
These changes should not affect you if you are only consuming LensKit’s algorithm and evaluation capabilities.
CSRclass to be more ergonomic from Python, at the expense of making the NumPy jitclass indirect. It is available in the
.Nattribute. Big improvement: it is now picklable.
LensKit 0.5.0 modifies the algorithm APIs to follow the SciKit design patterns instead of our previous custom patterns. Highlights of this change:
Algorithms are trained in-place — we no longer have distinct model objects.
Model data is stored as attributes on the algorithm object that end in
Instead of writing
model = algo.train_model(ratings), call
We also have some new capabilities:
Ben Frederickson’s Implicit library
A number of improvements, including replacing Cython/OpenMP with Numba and adding ALS.
A lot of fixes to get ready for RecSys.