bars
BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
Science Score: 41.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.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 (8.2%) to scientific vocabulary
Keywords
Repository
BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
Basic Info
Statistics
- Stars: 377
- Watchers: 4
- Forks: 60
- Open Issues: 2
- Releases: 0
Topics
Metadata Files
README.md
BARS
BARS is a project aimed for open BenchmArking for Recommender Systems: https://openbenchmark.github.io/BARS
Despite the significant progress made in both research and practice of recommender systems over the past two decades, there is a lack of a widely-recognized benchmark in this field. This not only increases the difficulty in reproducing existing studies, but also incurs inconsistent experimental results among them, which largely limit the practical value and potential impact of research in this field. In this project, we make our initiative efforts towards open benchamrking for recommender systems. The BARS benchmark project allows anyone to easily follow and contribute, and hopefully drive more solid and reproducible research on recommender systems.
The BARS benchmark currently covers the following two tasks.
- BARS-CTR: An Open Benchmark for CTR Prediction
- BARS-Match: An Open Benchmark for Candidate Item Matching
Ongoing projects:
- BARS-Rerank: An Open Benchmark for Listwise Reranking
- BARS-MTL: An Open Benchmark for Multi-Task Recommendation
🔥 Citation
If you find our benchmarks helpful in your research, please kindly cite the following paper.
Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang. BARS: Towards Open Benchmarking for Recommender Systems. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022. [Bibtex]
Contributing
We welcome any contribution that could help improve the BARS benchmark. Check the start guide on how to contribute.
Discussion
If you have any questions or feedback about the BARS benchamrk, please open a new issue or join our WeChat group.
Owner
- Name: RECZOO
- Login: reczoo
- Kind: organization
- Repositories: 1
- Profile: https://github.com/reczoo
Open Science by XUEPAI
Citation (CITATION)
@incollection{BARS,
author = {Jieming Zhu and
Quanyu Dai and
Liangcai Su and
Rong Ma and
Jinyang Liu and
Guohao Cai and
Xi Xiao and
Rui Zhang},
title = {BARS: Towards Open Benchmarking for Recommender Systems},
booktitle = {The 45th International ACM SIGIR Conference on Research
and Development in Information Retrieval (SIGIR'22)},
year = {2022}
}
GitHub Events
Total
- Issues event: 3
- Watch event: 25
- Issue comment event: 2
- Push event: 1
- Fork event: 3
Last Year
- Issues event: 3
- Watch event: 25
- Issue comment event: 2
- Push event: 1
- Fork event: 3
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 55
- Total pull requests: 0
- Average time to close issues: about 1 month
- Average time to close pull requests: N/A
- Total issue authors: 26
- Total pull request authors: 0
- Average comments per issue: 2.02
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 13
- Pull requests: 0
- Average time to close issues: 14 days
- Average time to close pull requests: N/A
- Issue authors: 5
- Pull request authors: 0
- Average comments per issue: 0.69
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- zhujiem (3)
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- lemyx (2)
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- JoshonSmith (2)
- discivigour (2)
- Aliang-CN (1)
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- Xueqi-Li (1)
- junkangwu (1)
- lsquser (1)
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- Isuxiz (1)
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Dependencies
- numpy *
- scikit-learn *
- scipy *
- tensorflow *
- toolz *
- tqdm *