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FML

Implementation of the IEEE TII paper titled "Unraveling Metric Vector Spaces withFactorization for Recommendation"

Install / Use

/learn @cheungdaven/FML
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

Use Python 3.5 or higher version.

The revision of this paper titled "Unraveling Metric Vector Spaces withFactorization for Recommendation" has been accepted by IEEE Transactions on Industrial Informatics.

If you use this code, please cite the following paper. Thank you very much.

@ARTICLE{8867947,
author={S. {Zhang} and L. {Yao} and B. {Wu} and X. {Xu} and X. {Zhang} and L. {Zhu}},
journal={IEEE Transactions on Industrial Informatics},
title={Unraveling Metric Vector Spaces with Factorization for Recommendation},
year={2019},
volume={},
number={},
pages={1-1},
keywords={Matrix converters;Task analysis;Extraterrestrial measurements;Sparse matrices;Informatics;Euclidean distance;Recommender Systems;Matrix Factorization;Collaborative Filtering},
doi={10.1109/TII.2019.2947112},
ISSN={},
month={},}
View on GitHub
GitHub Stars92
CategoryDevelopment
Updated2d ago
Forks33

Languages

Python

Security Score

80/100

Audited on Mar 29, 2026

No findings