/Legommenders

A modular recommendation system that allows the selection of different components to be combined into a new recommender.

Primary LanguagePythonMIT LicenseMIT

Legommenders

A modular framework for recommender systems

Legommenders

Updates

Jan. 23, 2024

  • Legommenders partially supports the flatten sequential recommendation model.
  • New models are added, including: MaskNet, GDCN, etc.

Oct. 16, 2023

  • We clean the code and convert names of the item-side parameters.

Oct. 5, 2023

  • The first recommender system package, Legommenders, with a modular-design is released!
  • Legommenders involves a set of recommender system algorithms, including:
    • Matching based methods: NAML, NRMS, LSTUR, etc.
    • Ranking based methods: DCN, DeepFM, PNN, etc.

Citations

Legommenders have served as a fundamental framework for several research projects, including ONCE, SPAR,GreenRec, and UIST. If you find Legommenders useful in your research, please consider citing our project:

@online{legommenders,
  author = {Liu, Qijiong},
  title = {Legommenders: A Modular Framework for Recommender Systems},
  year = {2023},
  url = {https://github.com/Jyonn/Legommenders}
}