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Contributors to Recommenders

Recommenders is developed and maintained by a community of people interested in exploring recommendation algorithms and how best to deploy them in industry settings. The goal is to accelerate the workflow of any individual or organization working on recommender systems. Everyone is encouraged to contribute at any level to add and improve the implemented algorithms, notebooks and utilities.

Committers (sorted alphabetically)

Committers are actively supporting the project and have made substantial contributions to the repository.
They have write access to the repo and provide support reviewing issues and pull requests.

  • Andreas Argyriou
    • SAR single node improvements
    • Reco utils metrics computations
    • Tests for Surprise
  • Dan Ciborowski
    • ALS operationalization notebook
    • SAR PySpark improvement
  • Markus Cosowicz
    • SAR improvements on Spark
  • Miguel González-Fierro
    • Recommendation algorithms review, development and optimization.
    • Reco utils review, development and optimization.
    • Github statistics.
    • Continuous integration build / test setup.
  • Scott Graham
    • Improving documentation
    • VW notebook
  • Nikhil Joglekar
    • Improving documentation
    • Quick start notebook
    • Operationalization notebook
  • Max Kaznady
    • Early SAR single node code and port from another internal codebase
    • Early SAR on Spark-SQL implementation
    • SAR notebooks
    • SAR unit / integration / smoke tests
    • Early infrastructure design based on collapsing another internal project
  • Jianxun Lian
    • xDeepFM algorithm
    • DKN algorithm
  • Jun Ki Min
    • ALS notebook
    • Wide & Deep algorithm
  • Jeremy Reynolds
    • Reference architecture
  • Mirco Milletarì
    • Restricted Boltzmann Machine algorithm
  • Tao Wu
    • Improving documentation
  • Le Zhang
    • Reco utils
    • Continuous integration build / test setup
    • Quickstart, deep dive, algorithm comparison, notebooks

Contributors

Full List of Contributors

  • To contributors: please add your name to the list when you submit a patch to the project
  • Aaron He
    • Reco utils of NCF.
    • Deep dive notebook demonstrating the use of NCF.
  • Nicolas Hug
    • Jupyter notebook demonstrating use of Surprise library for recommendations