Hierarchical Poisson Factorization

This module provides a LensKit bridge to the hpfrec library implementing hierarchical Poisson factorization [GHB13].

To install, 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: MFPredictor

Hierarchical Poisson factorization, provided by hpfrec.

Todo

Right now, this uses the ‘rating’ as a count. Actually use counts.

Parameters:

• features (int) – the number of features

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

fit(data, **kwargs)

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

Parameters:

• data (Dataset) – The training 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