Metadata-Version: 2.3
Name: easy-recommender
Version: 0.1.0
Summary: A simple recommendation system using implicit library
License: MIT
Keywords: recommendation,machine learning,collaborative filtering
Author: Your Name
Author-email: your.email@example.com
Requires-Python: >=3.12
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Provides-Extra: dev
Requires-Dist: implicit (>=0.7.0)
Requires-Dist: ipykernel (>=6.0.0) ; extra == "dev"
Requires-Dist: jupyter (>=1.0.0) ; extra == "dev"
Requires-Dist: jupyterlab (>=4.0.0) ; extra == "dev"
Requires-Dist: lightfm (>=1.17)
Requires-Dist: numpy (>=1.24.0)
Requires-Dist: pandas (>=2.0.0)
Requires-Dist: pytest (>=7.0.0) ; extra == "dev"
Requires-Dist: scikit-learn (>=1.3.0)
Project-URL: Homepage, https://github.com/t-kabaya/easy-recommender
Project-URL: Repository, https://github.com/t-kabaya/easy-recommender
Description-Content-Type: text/markdown

# Easy Recommender

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

```bash
pip install easy-recommender
```

## Quick Start

```python
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
