-
Prepare the tensorflow-1.15 environment.
-
Go to the
PGE
package and usepython3 plainagg.py --dataset CiteULike --emb node2vec
to construct PGE for your dataset. -
Go to the
GCGAN
package and run GAR-GNN and GAR-MLP-
GAR-GNN:
python3 main.py --gpu_id 0 --dataset CiteULike --embed_meth node2vec --gan_model gargnn --sim_coe 0.05 --alpha 0.9
-
GAR-MLP:
python3 main.py --gpu_id 0 --dataset CiteULike --embed_meth node2vec --agg_meth none --gan_model garmlp --real_lys [200,200] --real_act tanh --sim_coe 0.1
-
@inproceedings{10.1145/3477495.3531897,
author = {Chen, Hao and Wang, Zefan and Huang, Feiran and Huang, Xiao and Xu, Yue and Lin, Yishi and He, Peng and Li, Zhoujun},
title = {Generative Adversarial Framework for Cold-Start Item Recommendation},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531897},
doi = {10.1145/3477495.3531897},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2565โ2571},
numpages = {7},
keywords = {adversarial framework, cold-start recommendation, recommender system},
location = {Madrid, Spain},
series = {SIGIR '22}
}