ADMM SLIM

This is experimental implementation of ADMM SLIM based on:

ADMM SLIM: Sparse Recommendations for Many Users in WSDM 2020.

You can use two types of SLIM model:

• ADMM SLIM
• Dense SLIM

Usage

1. Clone this repository in the directory where your script is placed.

$ git clone https://github.com/tnakae/admm_slim/

2. Import package in your script and use it.

import numpy as np
from sklearn.model_selection import train_test_split

from admm_slim import AdmmSlim, DenseSlim

# Generate Sample Data
# Row : User, Column : Item
shape = [100, 40]
X = np.where(np.random.randn(*shape) > 1.0, 1, 0)

# Split data to train/test
X_train, X_test = train_test_split(X)

# Fit ADMM SLIM
# Change AdmmSlim to DenseSlim if you want to use Dense SLIM model
model = AdmmSlim()
model.fit(X_train)

# Predict
y_predict = model.predict(X_test)

# Top-n Item recommendation
y_top_10 = model.recommend(X_test, top=10)

Example

See sample notebook