Zero

Mangaki's recommendation algorithms.

They are tested on Python 3.6, 3.7, 3.8 over OpenBLAS LP64 & MKL.

Install

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Usage

To run cross-validation:

1. Download a dataset like Movielens 100k.
2. Ensure the columns are named user,item,rating:

For example, here, user 3 gave 4.5 stars to item 5.

3. Then run:

   python compare.py <path/to/dataset>

You can tweak the experiments/default.json file to compare other models.

Custom usage

Most models have the following routines:

from zero.als import MangakiALS
model = MangakiALS(nb_components=10)
model.fit(X, y)
model.predict(X)

where:

• X is a numpy array of size nb_samples x 2 (first column: user ID, second column: item ID)
• and y is the column of ratings.

There are a couple of other methods that can be used for online fit, say model.predict_single_user(work_ids, user_parameters).

See zero.py as an example of dumb baseline (only predicts zeroes) to start from.

Results

Mangaki data

Movielens data

Feel free to use. Under MIT license.