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AI Tool Profile
Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time.
GitHub Link
The GitHub link is https://github.com/jylevangeline/mosrIntroduce
The GitHub repository "JYLEvangeline/MOSR" presents an algorithmic solution, MOSR, for the dynamic email re-ranking problem, as accepted by KDD '23. The paper's link and dataset download address are provided. The code for the solution involves generating a .json file through "check_data_distance_2.py" and running "model_new.py" with specific parameters for EnronA and EnronB datasets. The necessary dataset files are included in the repository. Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time.Content
Our paper could be viewd at (https://dl.acm.org/doi/pdf/10.1145/3580305.3599909) The dataset could be download in https://www.cs.cmu.edu/~enron/. Please put it in data/ data/organization2.csv is crawled by us. And it is already in data/ folder. python model_new.py --md 10 -v 0 -lr 0.99 python model_new.py --md 10 -v 1 -lr 0.99
Key facts
| Pricing model | Free |
|---|---|
| Free tier | Not verified |
| Starting price | Not verified |
| Data residency | Not verified |
| Compliance (as stated by vendor) | Not verified |
| Integrations | Not verified |
| Public API | Not verified |
| Self-hostable | Not verified |
| Official site | Online (auto-checked 2026-09-24) |
| Last verified | not yet |
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Update history
- 2026-09-24 · site status updated (auto-check)
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