gar's Introduction

GAR - Generative Adversarial Framework for Cold-Start Item Recommendation

Usage

Prepare the tensorflow-1.15 environment.
Run GAR:
- Go to the project directory and run GAR-MLP through
  
  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 --alpha 0.9
  
  python3 main.py --gpu_id 0 --dataset XING --embed_meth node2vec --agg_meth none --gan_model garmlp --real_lys [200,200] --real_act tanh --sim_coe 0.1 --alpha 0.9
- For GAR-GNN, firstly go to the PGE directory and use python3 plainagg.py --dataset name_of_dataset --emb node2vec to construct the graph embeddings. Then go back to the project directory and run GAR-GNN through
  
  python3 main.py --gpu_id 0 --dataset CiteULike --embed_meth node2vec --gan_model gargnn --sim_coe 0.05 --alpha 0.9
  
  python3 main.py --gpu_id 0 --dataset XING --embed_meth node2vec --gan_model gargnn --sim_coe 0.1 --alpha 0.9

Cite

@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 = {2565–2571},
	numpages = {7},
	keywords = {adversarial framework, cold-start recommendation, recommender system},
	location = {Madrid, Spain},
	series = {SIGIR '22}
}

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