It's not entirely clear what algorithm does
python-recsys implement, and how appropriate it is for the task at hand. It does provide metrics for rank-based evaluation, which suggests that it is at least somewhat applicable to the implicit feedback setting.
However, is is worth noting that the last commit in the
python-recsys repository was added in November 2014. In light of this, I would suggest some alternative implementations with are (1) more actively maintained and (2) more suited for the implicit feedback problem:
- Implicit (
benfred/implicit on Github): a fast Python implementation of a classic weighted alternative least squares algorithm for implicit feedback.
- LightFM (
lyst/lightfm on Github): a fast Python implementation of a number of learning-to-rank algorithms for implicit feedback.
- Spotlight (
maciejkula/spotlight): a neural-network toolkit for both implicit and explicit recommender models.
(Disclosure, I am the author of both LightFM and Spotlight.)
You can find a more exhaustive list of recommender libraries at the RecSys Wiki.