LightGT: A Light Graph Transformer for Multimedia Recommendation

This is our Pytorch implementation for the LightGT:

Yinwei Wei, Wenqi Liu, Fan Liu, Xiang Wang, Liqiang Nie and Tat-Seng Chua (2023). LightGT: A Light Graph Transformer for Multimedia Recommendation. In ACM SIGIR`23, Taipei, July. 23-27, 2023

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{LightGT,
  title     = {LightGT: A Light Graph Transformer for Multimedia Recommendation},
  author    = {Wei, Yinwei and
               Liu, Wenqi and 
               Liu, Fan and 
               Wang, Xiang and 
               Nie, Liqiang and 
               Chua, Tat-Seng},
  booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages     = {1508–1517},
  year      = {2023}
}

Environment Requirement

The code has been tested running under Python 3.8.15. The required packages are as follows:

• Pytorch == 1.7.0
• numpy == 1.23.4

Example to Run the Codes

The instruction of commands has been clearly stated in the codes.

• Movielens dataset
  python main.py --l_r=1e-2 --weight_decay=1e-2 --src_len=50 --score_weight=0.05 --nhead=1 --transformer_layers=4 --batch_size=2048 --lightgcn_layers=4 --dataset=movielens

• Tiktok dataset
  python main.py --l_r=1e-2 --weight_decay=1e-2 --src_len=50 --score_weight=0.05 --nhead=1 --transformer_layers=4 --batch_size=2048 --lightgcn_layers=4 --dataset=tiktok

• Kwai dataset
  python main.py --l_r=1e-2 --weight_decay=1e-2 --src_len=50 --score_weight=0.05 --nhead=1 --transformer_layers=4 --batch_size=2048 --lightgcn_layers=4 --dataset=kwai

Dataset

You can find the full version of recommendation datasets via Kwai, Tiktok, and Movielens. Since the copyright of datasets, we cannot release them directly.

	#Interactions	#Users	#Items	Visual	Acoustic	Textual
Movielens	1,239,508	55,485	5,986	2,048	128	100
Tiktok	726,065	36,656	76,085	128	128	128
Kwai	1,664,305	22,611	329,510	2,048	-	100

It is worth noting that MMGCN provides corresponding toy datasets that can be used for research.

-train.npy Train file. Each line is a user with her/his positive interactions with items: (userID and micro-video ID)
-val.npy Validation file. Each line is a user several positive interactions with items: (userID and micro-video ID)
-test.npy Test file. Each line is a user with several positive interactions with items: (userID and micro-video ID)