A simple recommendation system using implicit library
Project description
Easy Recommender
⚠️ This package is currently broken and under repair. Please do not use. ⚠️
This project only works with Python 3.11.11 because LightFM requires this specific version. Other versions are not supported.
A simple and efficient recommendation system library using implicit collaborative filtering and LightFM.
Features
- Simple API for building recommendation systems
- Support for both implicit and explicit feedback
- Built on top of proven libraries (implicit, LightFM)
- Easy data preprocessing utilities
Installation
pip install easy-recommender
Quick Start
from easy_recommender import recommend, process_df, build_feature_data
import pandas as pd
# Load your data
df = pd.read_csv('your_ratings.csv')
# Process the data
processed_df = process_df(df)
# Build features
user_features, item_features = build_feature_data(df)
# Get recommendations
recommendations = recommend(
processed_df,
user_features,
item_features,
user_id=123,
num_recommendations=10
)
print(recommendations)
Requirements
- Python >=3.12
- pandas >=2.0.0
- scikit-learn >=1.3.0
- numpy >=1.24.0
- implicit >=0.7.0
- lightfm >=1.17
License
MIT License
References
This implementation is based on the approach described in: https://zenn.dev/genda_jp/articles/2c2a1b5d185741
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