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MRG: Multimodal Review Generation

This is the code for the paper:

Multimodal Review Generation for Recommender Systems
Quoc-Tuan Truong and Hady W. Lauw
Presented at WWW 2019

We provide:

  • Code to train and evaluate the model
  • Sample data to run an experiment with MRG

If you find the code and data useful in your research, please cite:

@inproceedings{truong2019mrg,
 title={Multimodal Review Generation for Recommender Systems},
 author={Truong, Quoc-Tuan and Lauw, Hady W},
 booktitle={The World Wide Web Conference, {WWW} 2019}
 year={2019},
}

Requirements

  • Python 3
  • Tensorflow >=1.12,<2.0
  • Hickle
  • Tqdm
  • GloVe word embeddings

How to run

python train.py --data_dir ./data --batch_size 64 --learning_rate 0.001 --num_epochs 20

Training arguments:

python train.py --help
optional arguments:
  -h, --help            show this help message and exit
  --data_dir            DATA_DIR
                        Path to the data directory
  --learning_rate       LEARNING_RATE
                        Learning rate (default: 3e-4)
  --dropout_rate        DROPOUT_RATE
                        Probability of dropping neurons (default: 0.2)
  --lambda_reg          LAMBDA_REG
                        Lambda hyper-parameter for regularization (default: 1e-4)
  --num_epochs          NUM_EPOCHS
                        Number of training epochs (default: 20)
  --batch_size          BATCH_SIZE
                        Batch size of reviews (default: 64)
  --num_factors         NUM_FACTORS
                        Number of latent factors for users/items (default: 256)              
  --word_dim            WORD_DIM
                        Word embedding dimensions (default: 200)
  --lstm_dim            LSTM_DIM
                        Hidden dimensions of the LSTM Cell (default: 256)
  --max_length          MAX_LENGTH
                        Maximum length of reviews to be generated (default: 20)
  --display_step        DISPLAY_STEP
                        Display info after number of steps (default: 10)
  --allow_soft_placement ALLOW_SOFT_PLACEMENT
                        Allow device soft device placement

Contact

Questions and discussion are welcome: www.qttruong.info

About

Code of the paper "Multimodal Review Generation for Recommender Systems", WWW'19

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