https://github.com/google-research/recsim_ng
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.7%) to scientific vocabulary
Keywords
Repository
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Basic Info
- Host: GitHub
- Owner: google-research
- License: apache-2.0
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://github.com/google-research/recsim_ng
- Size: 3.69 MB
Statistics
- Stars: 120
- Watchers: 7
- Forks: 17
- Open Issues: 5
- Releases: 0
Topics
Metadata Files
README.md
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; an XLA-based vectorized execution model for running simulations on accelerated hardware; and tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem. Specifically, we present a collection of use cases that demonstrate how the functionality described above can help both researchers and practitioners easily develop and train novel algorithms for recommender systems. Please refer to Mladenov et al for the high-level design of RecSim NG. Please cite the paper if you use the code from this repository in your work.
Bibtex
@article{mladenov2021recsimng,
title = {RecSim {NG}: Toward Principled Uncertainty Modeling for Recommender Ecosystems},
author = {Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier}
year = {2021},
eprint={2103.08057},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Disclaimer
This is not an officially supported Google product.
Installation and Sample Usage
It is recommended to install RecSim NG using (https://pypi.org/project/recsim_ng).
shell
pip install recsim_ng
Here are some sample commands you could use for testing the installation:
git clone https://github.com/google-research/recsim_ng
cd recsim_ng/recsim_ng/applications/ecosystem_simulation
python ecosystem_simulation_demo.py
Tutorials
To get started, please check out our Colab tutorials. In RecSim NG: Basics, we introduce the RecSim NG model and corresponding modeling APIs and runtime library. We then demonstrate how we define a simulation using entities, behaviors, and stories. Finally, we illustrate differentiable simulation including model learning and inference.
In RecSim NG: Dealing With Uncertainty, we explicitly address the stochastics of the Markov process captured by a DBN. We demonstrate how to use Edward2 in RecSim NG and show how to use the corresponding RecSim NG APIs for inference and learning tasks. Finally, we showcase how the uncertainty APIs of RecSim NG can be used within a recommender-system model-learning application.
Documentation
Please refer to the white paper for the high-level design.
Owner
- Name: Google Research
- Login: google-research
- Kind: organization
- Location: Earth
- Website: https://research.google
- Repositories: 226
- Profile: https://github.com/google-research
GitHub Events
Total
- Issues event: 1
- Watch event: 5
Last Year
- Issues event: 1
- Watch event: 5
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| RecSim Team | n****y@g****m | 9 |
| ChihWei Hsu | c****u@g****m | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 7
- Total pull requests: 0
- Average time to close issues: 2 days
- Average time to close pull requests: N/A
- Total issue authors: 7
- Total pull request authors: 0
- Average comments per issue: 1.71
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- DaiYijia02 (1)
- ehtsham (1)
- soonjune (1)
- aSeriousCoder (1)
- shawnye2000 (1)
- Sounegi (1)
- ckchow (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 37 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 5
- Total maintainers: 1
pypi.org: recsim-ng
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
- Homepage: https://github.com/google-research/recsim_ng
- Documentation: https://github.com/google-research/recsim_ng
- License: Apache 2.0
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Latest release: 0.1.2
published almost 4 years ago
Rankings
Maintainers (1)
Dependencies
- absl-py *
- edward2 *
- gin-config *
- gym *
- jax *
- matplotlib *
- numpy *
- tensorflow *
- tensorflow-probability *