m.maheri
/
MetaTL
Watch 1
Star 0
Fork 0
Code Issues 0 Pull Requests 0 Releases 0 Wiki Activity
Sequential Recommendation for cold-start users with meta transitional learning
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
20 Commits
2 Branches
Tree: de53dda45f
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'de53dda45f'
${ noResults }
mohamad maheri de53dda45f Implementing Robustify Negative sampling and undo prepossessing 2 years ago
data/electronics first commit 3 years ago
.gitignore first commit 3 years ago
README.md first commit 3 years ago
hyper_main.py hyper parameter tuning including neg_size 2 years ago
hyper_tunning.py hyper parameter tuning including neg_size 2 years ago
main.py Implementing Robustify Negative sampling and undo prepossessing 2 years ago
models.py Implementing Robustify Negative sampling and undo prepossessing 2 years ago
sampler.py Implementing Robustify Negative sampling and undo prepossessing 2 years ago
trainer.py Implementing Robustify Negative sampling and undo prepossessing 2 years ago
utils.py Implementing Robustify Negative sampling and undo prepossessing 2 years ago

README.md

Sequential Recommendation for Cold-start Users with Meta Transitional Learning(SIGIR2021)

CuRe

Code of paper “Sequential Recommendation for Cold-start Users with Meta Transitional Learning”.

Requirements

python==3.6.8

Usage


## Cite

Please cite our paper if you use this code in your own work:

@inproceedings{wang2021sequential, title={Sequential Recommendation for Cold-start Users with Meta Transitional Learning}, author={Wang, Jianling and Ding, Kaize and Caverlee, James}, booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, pages={1783--1787}, year={2021} } ```