# Easy Recommender
⚠️ **This package is currently broken and under repair. Please do not use.** ⚠️
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
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"description": "# Easy Recommender\n\n\u26a0\ufe0f **This package is currently broken and under repair. Please do not use.** \u26a0\ufe0f\n\nA simple and efficient recommendation system library using implicit collaborative filtering and LightFM.\n\n## Features\n\n- Simple API for building recommendation systems\n- Support for both implicit and explicit feedback\n- Built on top of proven libraries (implicit, LightFM)\n- Easy data preprocessing utilities\n\n## Installation\n\n```bash\npip install easy-recommender\n```\n\n## Quick Start\n\n```python\nfrom easy_recommender import recommend, process_df, build_feature_data\nimport pandas as pd\n\n# Load your data\ndf = pd.read_csv('your_ratings.csv')\n\n# Process the data\nprocessed_df = process_df(df)\n\n# Build features\nuser_features, item_features = build_feature_data(df)\n\n# Get recommendations\nrecommendations = recommend(\n processed_df, \n user_features, \n item_features, \n user_id=123, \n num_recommendations=10\n)\n\nprint(recommendations)\n```\n\n## Requirements\n\n- Python >=3.12\n- pandas >=2.0.0\n- scikit-learn >=1.3.0\n- numpy >=1.24.0\n- implicit >=0.7.0\n- lightfm >=1.17\n\n## License\n\nMIT License\n\n## References\n\nThis implementation is based on the approach described in:\nhttps://zenn.dev/genda_jp/articles/2c2a1b5d185741\n",
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