LightFM is a Python implementation of recommendation algorithms for implicit and explicit feedback. It allows item and user metadata to be incorporated into matrix factorization algorithms, representing users and items as sums of latent feature representations. Installation is available from PyPI, and a Docker setup is provided for running examples and tests.
Auto ViML
Auto ViML automatically builds multiple machine learning models with a single line of code. It was created by Ram Seshadri, and collaborators are welcome with permission. The tool is available on GitHub.
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0LightFM is a Python implementation of recommendation algorithms for implicit and explicit feedback. It allows item and user metadata to be incorporated into matrix factorization algorithms, representing users and items as sums of latent feature representations. Installation is available from PyPI, and a Docker setup is provided for running examples and tests.
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