Awesome
CoHeat
This project is a PyTorch implementation of "Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating", which is published at The Web Conference 2024.
Prerequisties
The implementation is based on Python 3.10 and PyTorch 2.0.1
A complete list of required packages can be found in the requirements.txt
file.
Please install the necessary packages before running the code.
Datasets
We use 3 datasets in our work: Youshu, NetEase, and iFashion.
The preprocessed dataset is included in the repository: ./data
.
We separate the dataset into three scenarios: cold, warm, and all.
Configuration
To customize the configuration, please edit the ./src/config.yaml
file.
For guidance on setting the hyperparameters, please refer to our paper.
Running the code
To execute the code, use the command python main.py
with the arguments --data
and --seed
.
For convenience, we provide a demo.sh
script that reproduces the experiments presented in our work.
Citation
Please cite this paper when you use our code.
@inproceedings{conf/www/JeonLYK24,
author = {Hyunsik Jeon and
Jong-eun Lee and
Jeongin Yun and
U Kang},
title = {Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating},
booktitle = {WWW},
year = {2024},
}