- added recall@k evaluation function
- added >=0.17.0 scipy depdendency to setup.py
- fixed segfaults on when duplicate entries are present in input COO matrices (thanks to Florian Wilhelm for the bug report).
- fixed gradient accumulation in adagrad (the feature value is now correctly used when accumulating gradient). Thanks to Benjamin Wilson for the bug report.
- all interaction values greater than 0.0 are now treated as positives for ranking losses.
- max_sampled hyperparameter for WARP losses. This allows trading off accuracy for WARP training time: a smaller value will mean less negative sampling and faster training when the model is near the optimum.
- Added a sample_weight argument to fit and fit_partial functions. A high value will now increase the size of the SGD step taken for that interaction.
- Added an evaluation module for more efficient evaluation of learning-to-rank models.
- Added a random_state keyword argument to LightFM to allow repeatable model runs.
- By default, an OpenMP-less version will be built on OSX. This allows much easier installation at the expense of performance.
- The default value of the max_sampled argument is now 10. This represents a decent default value that allows fast training.
- fix scipy missing from requirements in setup.py
- remove dependency on glibc by including a translation of the musl rand_r implementation
- fixed bug where item momentum would be incorrectly used in adadelta training for user features (thanks to Jong Wook Kim @jongwook for the bug report).
- user and item features are now floats (instead of ints), allowing fractional feature weights to be used when fitting models.
- when installing into an Anaconda distribution, drop -march=native compiler flag due to assembler issues.
- when installing on OSX, search macports and homebrew install location for gcc version 5.x
- when installing on OSX, search macports install location for gcc
- input matrices automatically converted to correct dtype if necessary