Social Explorative Attention Networks (SEAN)

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.

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.

Basic Usage

Dataset download

English Files down load: please put files in the dir dataset/steemit/en/

processed_user_activity.json

https://1drv.ms/u/s!AorJby8-9jo1gy6SzwIpwEs2DdaM

new_article.json

https://1drv.ms/u/s!AorJby8-9jo1gy2MJPREepy6Sgjq

Example

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

Input

The supported input format is an edgelist:

node1_id_int node2_id_int

The graph is assumed to be directed and unweighted by default.

Output

The probability of clicking an unseen document by the target user.

Citing

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}
}

Miscellaneous

Please send any questions you might have about the code and/or the algorithm to wxiaoae@cse.ust.hk.

hzhaoaf/Social-Explorative-Attention-Networks-1