Hierarchical Poisson Factorization

This module provides a LensKit bridge to the hpfrec library implementing hierarchical Poisson factorization :cite:p:`Gopalan2013-ko`.

To install (after we have cut a release), run:

pip install lenskit-hpf

We do not provide a Conda package, because hpfrec is not packaged for Conda. You can use pip to install this package in your Anaconda environment after installing LensKit itself with conda.

class lenskit_hpf.HPF(features, **kwargs)

Bases: lenskit.algorithms.mf_common.MFPredictor

Hierarchical Poisson factorization, provided by hpfrec.

Parameters

  • features (int) – the number of features

  • **kwargs – arguments passed to hpfrec.HPF.

fit(ratings, **kwargs)

Train a model using the specified ratings (or similar) data.

Parameters

  • ratings (pandas.DataFrame) – The ratings data.

  • kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

Returns

The algorithm object.

predict_for_user(user, items, ratings=None)

Compute predictions for a user and items.

Parameters

  • user – the user ID

  • items (array-like) – the items to predict

  • ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.

Returns

scores for the items, indexed by item id.

Return type

pandas.Series