Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Jinjiarui

Last update: Nov 24, 2022

Related tags

Deep Learning GraphHINGE

Overview

Heterogeneous INteract and aggreGatE (GraphHINGE)

This is a pytorch implementation of GraphHINGE model. This is the experiment code in the following work:

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola.
KDD 2020 Oral

and its extended work:

Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin, Yuchen Fang, Yong Yu, Zheng Zhang, Alexander J. Smola.
TOIS

Prerequisites

Python 3.6
Pytorch 1.8.0
DGL 0.6.0

References

If you find this work helpful in your research, please consider citing the following paper. The bibtex are listed below:

@inproceedings{jin2020efficient,
  title={An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph},
  author={Jin, Jiarui and Qin, Jiarui and Fang, Yuchen and Du, Kounianhua and Zhang, Weinan and Yu, Yong and Zhang, Zheng and Smola, Alexander J},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={75--84},
  year={2020}
}

@article{jin2020learning,
  title={Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network},
  author={Jin, Jiarui and Du, Kounianhua and Zhang, Weinan and Qin, Jiarui and Fang, Yuchen and Yu, Yong and Zhang, Zheng and Smola, Alexander J},
  journal={arXiv preprint arXiv:2011.12683},
  year={2020}
}

Comments

About Potential Data Leakage

I noticed that you used all “user-item” edges to construct the heterogeneous graph. And based on it, you then predict the user-item edges (i.e. recommendation). Would this cause data leakage?

opened by aHuiWang 2
experiments results in KDD paper and "GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network"

您好，感谢您在HIN领域出色的工作！我了解了您在KDD 2020的paper以及后面这篇paper中与KDDpaper中方法相似的部分，以GraphHINGE_FFT.py中的方法为例，kDD论文中movielens的acc为0.7896，后一篇为0.8837，我看主要方法部分没有很明显的区别（仅指在这个py文件中所涉及的网络结构），而且baseline的相同方法也有一定程度的提升，请问这种提升是因为什么呢？是因为hyperparameter的设置，数据的预处理方式，还是因为一些两篇论文细节方面我没有充分进行理解，在细节上是有区别的（仅指在KDD论文中提到的方法)？我是这个方向的初学者，如果您能对我的疑惑进行解答，我将非常感谢！

opened by zhj123169 1
请教一下有关movielens数据集预处理的相关问题

感谢您在HIN领域作出的贡献！我在运行您的代码dataloader.py 的 _load_movielens(self)时，想知道您运行时导入的 _data_list = ['movie_genre.dat', 'user_occupation.dat', 'user_age.dat', 'user_movie.dat'] 四个文件是如何通过原始数据预处理得到的对于其中的movie_genre.dat _movie, _genre = int(_line[0]), int(_line[1]) 是如何将类型变为数字的呢？如果您能为我解答或者将您处理后并且实际测试的movielens数据集提供给我，我将感激不尽，谢谢！

opened by zhj123169 1
About baseline methods

您好，请问能否提供您选用的一些baseline方法的代码呢，因为我发现许多的源码具有特异性，而我刚入门这个领域，对于如何改写这些模型感到困惑（例如LGRec和MCRec它们的initial embedding的详细生成方法并未提供，而且LGRec的模型代码似乎跑不通；HetGNN模型是hard coded for academic paper，怎么改用到其他数据呢？HAN模型是embedding方法，如果要改用到推荐，该如何实现呢？）希望能得到您的回复

opened by Chuan1997 1
sampling path but without omitting existing edge

Imho, omitting 20% of existing edges should be done before constructing the graph when doing 5 fold cross-validation. Otherwise, meta-path sampling will generate a lot of paths involved with positive instances in the test&eval dataset, which is an information leak and should be unseen.

opened by dlutwy 1
Got an unexpected result

I downloaded the ml-100k(MovieLens 100K Dataset) and did the experiments with default args ( 3 epochs), however, the results I got are much higher than those reported in the paper.

Test AUC 0.9506 | ACC 0.8792 | Test F1 0.8764

I am wondering whether it is caused by my data processing scripts or other reason.

opened by dlutwy 1

Owner

Jinjiarui

Ph.D. Student at Apex Lab

GitHub

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Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Related tags

Overview

Heterogeneous INteract and aggreGatE (GraphHINGE)

Prerequisites

References

Comments

About Potential Data Leakage

experiments results in KDD paper and "GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network"

请教一下有关movielens数据集预处理的相关问题

About baseline methods

sampling path but without omitting existing edge

Got an unexpected result

Owner

Jinjiarui

TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

Inference code for "StylePeople: A Generative Model of Fullbody Human Avatars" paper. This code is for the part of the paper describing video-based avatars.

A code generator from ONNX to PyTorch code

This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

Convert Python 3 code to CUDA code.

Empirical Study of Transformers for Source Code & A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Low-code/No-code approach for deep learning inference on devices

Code for all the Advent of Code'21 challenges mostly written in python

Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

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