Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020

LI Xingchen

Last update: Dec 5, 2022

Related tags

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Overview

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.

Comments

data processing

Hi, "To ensure the quality of dataset, we only retain the users with at least 20 interactions and the outfits with at least 10 interactions". "the outfits with at least 10 interactions" refers to? I tried some methods but couldn't get numbers of user and outfit as said in the paper(53897 27694).

opened by lydiayl 0
How to reprocess fltb_test_file.txt file

Hi, Can anyone please tell how to reprocess fltb_test_file.txt,i dont understand what is mask_position.And how to determine pos_outfit and neg_outfit.

Thanks in Advance

opened by mahi214 0
能否详细说说数据集的预处理环节?

按照论文来看, 不知道我是不是理解错误, Outfit层中的信息来源是Item层, 那么在训练和测试时是否要确保每个outfit中的item都要在POG提供的item之中? 如果正确的话, POG中似乎又没有这么多保证全部item的outfit.

看了之前的issue, 我理解是随机采样得到的数据集, 可能没法完全复现论文的效果, 但是如果能贴出来给大家参考一下, 我觉得还是很有帮助的.

希望不吝赐教.

opened by fang2hou 0
Ask the datasets for "Hierarchical Fashion Graph Network for Personalized Outfit Recommendation"

Hello, I'm interested in your paper "Hierarchical Fashion Graph Network for Personalized Outfit Recommendation", and I am trying to learn the paper's code now. But I am so sorry that I cannot find the dataset files (train_uo, test_uo, TXT, flTB_train_outfit_item.txt, flTB_resnet152_02.npy, flTB_item_cate2.txt, fltb_test_file.txt). Could you please send me? Thank you very much.

opened by JingpingFeng 2
How can I process the dataset of POG? Can you release the data-processing file?

Can you release the file which is used to sampling the val dataset?

As said in the paper:"That’s because the negative samples in our test dataset is the remaining outfits that users haven’t clicked", some sampling tricks are used in the data processing. So I think these files are absolutely necessary。

opened by zengqixun12 8

Owner

LI Xingchen

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Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020

Related tags

Overview

hierarchical_fashion_graph_network

Introduction

Citation

Dataset

Environment

Run the Codes

Train the model

Comments

data processing

How to reprocess fltb_test_file.txt file

能否详细说说数据集的预处理环节?

Ask the datasets for "Hierarchical Fashion Graph Network for Personalized Outfit Recommendation"

How can I process the dataset of POG? Can you release the data-processing file?

Owner

LI Xingchen

A TensorFlow recommendation algorithm and framework in Python.

Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems.

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms

A framework for large scale recommendation algorithms.

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An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend.

Implementation of a hadoop based movie recommendation system

Books Recommendation With Python

Bert4rec for news Recommendation

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(ICCV 2021) Official code of "Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing."

Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 short.

Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

Hierarchical-Bayesian-Defense - Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference (Openreview)

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