matrix-factorization 1.1

Library for matrix factorization for recommender systems using collaborative filtering

Matrix Factorization

Short and simple implementation of kernel matrix factorization with online-updating for use in collaborative recommender systems.

Prerequisites

• Python 3
• numba
• numpy
• pandas
• scikit-learn
• scipy

Installation

pip install matrix_factorization

Usage

from matrix_factorization import BaselineModel, KernelMF, train_update_test_split

import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Movie data found here https://grouplens.org/datasets/movielens/
cols = ['user_id', 'item_id', 'rating', 'timestamp']
movie_data = pd.read_csv('../data/ml-100k/u.data', names = cols, sep = '\t', usecols=[0, 1, 2], engine='python')

X = movie_data[['user_id', 'item_id']]
y = movie_data['rating']

# Prepare data for online learning
X_train_initial, y_train_initial, X_train_update, y_train_update, X_test_update, y_test_update = train_update_test_split(movie_data, frac_new_users=0.2)

# Initial training
matrix_fact = KernelMF(n_epochs = 20, n_factors = 100, verbose = 1, lr = 0.001, reg = 0.005)
matrix_fact.fit(X_train_initial, y_train_initial)

# Update model with new users
matrix_fact.update_users(X_train_update, y_train_update, lr=0.001, n_epochs=20, verbose=1)
pred = matrix_fact.predict(X_test_update)
rmse = mean_squared_error(y_test_update, pred, squared = False)
print(f'\nTest RMSE: {rmse:.4f}')

# Get recommendations
user = 200
items_known = X_train_initial.query('user_id == @user')['item_id']
matrix_fact.recommend(user=user, items_known=items_known)

License

This project is licensed under the MIT License

References :book:

• Steffen Rendle, Lars Schmidt-Thieme. Online-updating regularized kernel matrix factorization models for large-scale recommender systems https://dl.acm.org/doi/10.1145/1454008.1454047