AttentionBasedMultiModalRNN

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AttentionBasedMultiModalRNN

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An unofficial Pytorch implementation of Attention based Multi-Modal New Product Sales Time-series Forecasting

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# Attention based Multi-Modal New Product Sales Time-series Forecasting

An unofficial Pytorch implementation of [**Attention based Multi-Modal New Product Sales Time-series Forecasting**](https://dl.acm.org/doi/10.1145/3394486.3403362) paper. We use multiple approaches from this code and the aforementioned paper in our work [**Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends**](https://github.com/HumaticsLAB/GTM-Transformer)

The repository contains the implementation of the following baselines:
- KNN baselines:
  - Attribute KNN
  - Image KNN
  - Attribute + Image KNN
- Networks:
  - Image RNN
  - Concat Multimodal RNN
  - Residual Multimodal RNN
  - Explainable Cross-Attention RNN

Thanks to [Nicholas Merci](https://github.com/nicholasmerci) and [Carlo Veronesi](https://github.com/carloveronesi) for the faithful implementation of the paper.

## Installation

We suggest the use of VirtualEnv.

```bash

python3 -m venv mmrnn_venv
source mmrnn_venv/bin/activate
# mmrnn_venv\Scripts\activate.bat # If you're running on Windows

pip install numpy pandas matplotlib opencv-python permetrics Pillow scikit-image scikit-learn scipy tqdm transformers fairseq wandb

pip install torch torchvision

#For CUDA11.1 (NVIDIA 3K Serie GPUs)
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

export INSTALL_DIR=$PWD

cd $INSTALL_DIR
git clone https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN.git
cd AttentionBasedMultiModalRNN
mkdir dataset

unset INSTALL_DIR
```

## Dataset

**VISUELLE** dataset is publicly available to download [here](https://forms.gle/cVGQAmxhHf7eRJ937). Please download and extract it inside the dataset folder.

## Run KNNs
To run the KNN models o please use the following scripts. Please check the arguments inside config.py and the dedicated arguments inside the script.

```bash
python KNN.py --exp_num 1 # Attribute KNN
python KNN.py --exp_num 2 # Image KNN
python KNN.py --exp_num 3 # Attribute+Image KNN
```

## Training
To train the model of the baselines please use the following scripts. Please check the arguments inside config.py

```bash
python train.py 
```

## Inference
To evaluate the model of the baselines please use the following scripts. Please check the arguments inside config.py

```bash
python infer.py
```

## Citation
If you use **VISUELLE** dataset or this paper implementation, please cite the following papers.

```
@misc{skenderi2021googled,
      title={Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends}, 
      author={Geri Skenderi and Christian Joppi and Matteo Denitto and Marco Cristani},
      year={2021},
      eprint={2109.09824},
}

```

```
@inbook{10.1145/3394486.3403362,
author = {Ekambaram, Vijay and Manglik, Kushagra and Mukherjee, Sumanta and Sajja, Surya Shravan Kumar and Dwivedi, Satyam and Raykar, Vikas},
title = {Attention Based Multi-Modal New Product Sales Time-Series Forecasting},
year = {2020},
isbn = {9781450379984},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394486.3403362},
booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining},
pages = {3110–3118},
numpages = {9}
}
```

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