Code for paper:
Bei Wang, Chenrui Zhang, Hao Zhang, Xiaoqing Lyu, Zhi Tang, Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation (CIKM2020)
In this paper, we propose an end-to-end Dual Autoencoder Network (DAN) for user cold-start recommendations with a pair of encoder-decoder networks. Conceptually, the proposed encoder in each domain adopts a graph neural network to embed the high-order collaborative information among users and items in the interaction graph via multi-hop propagation for effective user preference learning. The decoder transforms the user information to the other domain for recommendations.
- PyTorch
- dgl
- tensorboardX
The data set used in this paper is the public data set, which can be found on the website.
cd src
sh run.sh@inproceedings{wang2020dual,
title={Dual autoencoder network with swap reconstruction for cold-start recommendation},
author={Wang, Bei and Zhang, Chenrui and Zhang, Hao and Lyu, Xiaoqing and Tang, Zhi},
booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
pages={2249--2252},
year={2020}
}