Adversarial Training Towards Robust Multimedia Recommender System
Appending adversarial training on multimedia features enhances the performance of multimedia recommender system.
This is our official implementation for the paper:
Jinhui Tang, Xiangnan He, Xiaoyu Du, Fajie Yuan, Qi Tian, and Tat-Seng Chua, Adversarial Training Towards Robust Multimedia Recommender System.
If you use the codes, please cite our paper. Thanks!
- Tensorflow 1.7
- numpy, scipy
- f_resnet.npy Deep image features extracted with Resnet. The $i$-th row indicates the $i$-th item feature.
- pos.txt The training samples used in training process. The numbers $u$ and $i$ in each row indicate an interaction between user $u$ and item $i$.
- neg.txt The test samples used in testing process. The first number of row $u$ is the only positive sample in test, the following numbers of row $u$ are the negative samples for user $u$.
Pretrained VBPR The pretrained VBPR is stored in
The training logs are stored in
Source files are stored in
main.py. The main entrance of the program.
solver/*. The solvers managing the training process.
model/*. The models.
dataset/*. The data readers.