Mangaki's recommandation algorithms
Project description
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:
- Download a dataset like Movielens 100k.
- Ensure the columns are named
user,item,rating
:
user | item | rating |
---|---|---|
3 | 5 | 4.5 |
For example, here, user 3 gave 4.5 stars to item 5.
-
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.
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