https://github.com/kakao/buffalo

TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems
https://github.com/kakao/buffalo

cpp machine-learning matrix-factorization python recommender-system

Last synced: 3 days ago
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TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems

• Host: GitHub
• URL: https://github.com/kakao/buffalo
• Owner: kakao
• License: apache-2.0
• Created: 2019-08-27T14:56:48.000Z (over 5 years ago)
• Default Branch: dev
• Last Pushed: 2024-03-07T08:43:30.000Z (10 months ago)
• Last Synced: 2024-04-27T23:42:24.173Z (8 months ago)
• Topics: cpp, machine-learning, matrix-factorization, python, recommender-system
• Language: Python
• Homepage:
• Size: 11.9 MB
• Stars: 572
• Watchers: 27
• Forks: 106
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.MD
  • License: LICENSE

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# Buffalo

Buffalo is a fast and scalable production-ready open source project for recommender systems. Buffalo effectively utilizes system resources, enabling high performance even on low-spec machines. The implementation is optimized for CPU and SSD. Even so, it shows good performance with GPU accelerator, too. Buffalo, developed by Kakao, has been reliably used in production for various Kakao services.

For more information see the [documentation](https://buffalo-recsys.readthedocs.io)

## Requirements

- Python 3.8+

- cmake 3.17+

- gcc/g++ (with std=c++14)

## License

This software is licensed under the [Apache 2 license](LICENSE), quoted below.

Copyright 2020 Kakao Corp. 

Licensed under the Apache License, Version 2.0 (the "License"); you may not

use this project except in compliance with the License. You may obtain a copy

of the License at http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS, WITHOUT

WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the

License for the specific language governing permissions and limitations under

the License.