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.
Tags：Paper and LLMsPricing Type
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GitHub Link
The GitHub link is https://github.com/jylevangeline/mosr
Introduce
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
Related
LongLLaMA is a large language model designed to handle very long text contexts, up to 256,000 tokens. It's based on OpenLLaMA and uses a technique called Focused Transformer (FoT) for training. The repository provides a smaller 3B version of LongLLaMA for free use. It can also be used as a replacement for LLaMA models with shorter contexts.
Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers.
