google-research/recsim_ng
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
This project helps recommender system researchers and practitioners simulate complex, multi-agent recommender ecosystems. It takes descriptions of user and agent behaviors, runs detailed simulations, and outputs insights that can be used to develop and train new recommendation algorithms. Its users are typically data scientists, machine learning engineers, and researchers working on recommender systems.
125 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to build transparent, configurable, and end-to-end simulations of recommender systems to test new algorithms or understand user behavior with uncertainty.
Not ideal if you're looking for an off-the-shelf recommender system or a tool to deploy recommendations directly to users.
Stars
125
Forks
15
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Apr 26, 2022
Commits (30d)
0
Dependencies
9
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