This repository provides a reference implementation of sean as described in the paper in SIGKDD 2019:
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction.
Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang.
https://arxiv.org/abs/1905.11900
The sean algorithm goes beyond personalized content recommendation by considering both content creators and consumers, which motivates us to develop a highly personalized attention based model and explore higher-order social friends.
English Files down load: please put files in the dir dataset/steemit/en/
- processed_user_activity.json
https://drive.google.com/file/d/1QOELfVtgGgvPFGDpEI3-ZsgKlPxZX2fb/view?usp=sharing
- new_article.json
https://drive.google.com/file/d/1YQnb44R5t75D4t8XFWDArMkAllnbUm8S/view?usp=sharing
- processed_user_relation.json
https://drive.google.com/file/d/1qVV0Q65euh67Krkdw3UOkQTywmKxYdbC/view?usp=sharing
To run sean on Steemit-En, you can use the following command:
cd sean
python steemit_preprocessing
python payout.py --walk-length 10 --num-walks 3 --alpha 1
The supported input format is an edgelist:
node1_id_int node2_id_int
The graph is assumed to be directed and unweighted by default.
The probability of clicking an unseen document by the target user.
If you find sean useful for your research, please consider citing the following paper:
@inproceedings{sean-kdd2019,
author = {Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang.},
title = {Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction. },
booktitle = {Proceedings of the 25nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2019}
}
Please send any questions you might have about the code and/or the algorithm to wxiaoae@cse.ust.hk.