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!
Requirements
- Tensorflow 1.7
- numpy, scipy
Quick Start
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Data
- 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 $u$.
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Pretrained VBPR The pretrained VBPR is stored in
weights/best-vbpr.npy -
Traing AMR
bash run.shThe training logs are stored in
logs
Source Files
Source files are stored in src/.
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main.py. The main entrance of the program.
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solver/*. The solvers managing the training process.
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model/*. The models.
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dataset/*. The data readers.