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This is our official implementation for the paper: Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, and Tat-Seng Chua, Adversarial Training Towards Robust Multimedia Recommender System.
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README.md

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

figure.png

  1. 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 $u$.
  2. Pretrained VBPR The pretrained VBPR is stored in weights/best-vbpr.npy

  3. Traing AMR

    bash run.sh
    

    The training logs are stored in logs

Source Files

Source files are stored in src/.

  • main.py. The main entrance of the program.

  • solver/*. The solvers managing the training process.

  • model/*. The models.

  • dataset/*. The data readers.

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