Awesome
hierarchical_fashion_graph_network
This is our Tensorflow implementation for the paper:
Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and Tat-Seng Chua. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. In SIGIR 2020.
Introduction
Hierarchical Fashion Graph Network (HFGN) is a new recommendation framework for personalized outfit recommendation task based on hierarchical graph structure.
Citation
If you want to use our codes and datasets in your research, please cite:
@inproceedings{HFGN20,
author = {Xingchen Li and
Xiang Wang and
Xiangnan He and
Long Chen and
Jun Xiao and
Tat{-}Seng Chua},
title = {Hierarchical Fashion Graph Network for Personalized Outfit Recommendation},
booktitle = {Proceedings of the 43rd International {ACM} {SIGIR} Conference on
Research and Development in Information Retrieval, {SIGIR} 2020.},
year = {2020},
}
Dataset
Our experiment are based on POG dataset. We reprocess the data and save the files, and the file format is listed in Data/pog.
Environment
tensorflow == 1.10.1 python == 3.6
Run the Codes
python model.py -regs 1e-5 --embed_size 64 --batch_size 1024
Train the model
For Fill in the Blank (FLTB) task, we only optimize the compatibility loss: L_{com}.
For Personalized outfit Recommendation task, we use the pretrained FLTB model to intialized the whole model to obtain better performance.