The implement of papar "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization"

Overview

SIGIR2021-EGLN

The implement of paper "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization"

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users’ unobserved behaviors are treated as unlinked edges in the user-item bipartite graph. As users’ unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference links. In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other in CF, as updated node embeddings are learned from previous graph structure, and vice versa (i.e., newly updated graph structure are optimized based on current node embedding results). Some previous works provided approaches to refine the graph structure. However, most of these graph learning models relied on node features for modeling, which are not available in CF. Besides, nearly all optimization goals tried to compare the learned adaptive graph and the original graph from a local reconstruction perspective, whether the global properties of the adaptive graph structure are modeled in the learning process is still unknown. To this end, in this paper, we propose an enhanced graph learning network (EGLN ) approach for CF via mutual information maximization. The key idea of EGLN is two folds: First, we let the enhanced graph learning module and the node embedding module iteratively learn from each other without any feature input. Second, we design a local-global consistency optimization function to capture the global properties in the enhanced graph learning process. Finally, extensive experimental results on three real-world datasets clearly show the effectiveness of our proposed model.

Prerequisites

  • Tensorflow 1.15.0
  • Python 3.7.9

Usage

  • Dataset:
    Under the data folder(cd ./datasets)
  • Run model for amazon dataset:
    cd ./code/amazon_code python egln.py

Citation

If you find this useful for your research, please kindly cite the following paper:

@inproceedings{yang2021enhanced,
  title={Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization},
  author={Yang, Yonghui and Wu, Le and Hong, Richang and Zhang, Kun and Wang, Meng},
  booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={71--80},
  year={2021}
}

This work focus on graph structure learning via graph mutual infomax. If you are also interested in graph node attributes learning, you can refer to the following paper:

@inproceedings{wu2020joint,
  title={Joint item recommendation and attribute inference: An adaptive graph convolutional network approach},
  author={Wu, Le and Yang, Yonghui and Zhang, Kun and Hong, Richang and Fu, Yanjie and Wang, Meng},
  booktitle={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={679--688},
  year={2020}
}

Author contact:

Email: [email protected]

You might also like...
implement of SwiftNet:Real-time Video Object Segmentation

SwiftNet The official PyTorch implementation of SwiftNet:Real-time Video Object Segmentation, which has been accepted by CVPR2021. Requirements Python

a Pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" 1. Notes This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in

PyTorch Implement of Context Encoders: Feature Learning by Inpainting
PyTorch Implement of Context Encoders: Feature Learning by Inpainting

Context Encoders: Feature Learning by Inpainting This is the Pytorch implement of CVPR 2016 paper on Context Encoders 1) Semantic Inpainting Demo Inst

Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch
Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch

disclaimer: this code is modified from pytorch-tutorial Image classification with synthetic gradient in Pytorch I implement the Decoupled Neural Inter

Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Demonstration of OpenVINO techniques - Model-division and a simplest-way to support custom layers Description: Model Optimizer in Intel(r) OpenVINO(tm

Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas

This is the official implement of paper
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

This is the official implement of paper
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

Implement some metaheuristics and cost functions
Implement some metaheuristics and cost functions

Metaheuristics This repot implement some metaheuristics and cost functions. Metaheuristics JAYA Implement Jaya optimizer without constraints. Cost fun

Comments
  • val_ndcg

    val_ndcg

    Hi, I have one more question about your code

    line 558: _hr, _ndcg = evaluate(testdata, user_items, [5,10,15,20,25,30,35,40,45,50]) line 559: valndcg[epoch] = _ndcg[10]

    I could not understand why _ndcg[10] (input : testdata) is valndcg.. I think that i miss something... please let me know

    Best regards

    opened by MLATH 0
  • Comparison

    Comparison

    Hi, Thanks for making the code public! I have a single question about the model comparison. In the original paper both NGCF and LightGCN, the authors mentioned that they split the data via train/valid/test. But there are no validation data in the author's Github code while your EGLN has validation data.

    Did you conduct experiments on NGCF and LightGCN with your split data? or just the author's code? I think that NGCF and LightGCN are unfair since they use the test dataset to tune hyperparameters. But LightGCN still outperforms based on your experimental results.

    Thank you!

    opened by MLATH 0
Owner
null
Implement face detection, and age and gender classification, and emotion classification.

YOLO Keras Face Detection Implement Face detection, and Age and Gender Classification, and Emotion Classification. (image from wider face dataset) Ove

Chloe 10 Nov 14, 2022
offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

PeterPu 76 Dec 8, 2022
Official implement of Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images 深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测 Of

Chenxiao Zhang 135 Dec 19, 2022
Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

ImageProcessingTransformer Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

null 61 Jan 1, 2023
Official implement of "CAT: Cross Attention in Vision Transformer".

CAT: Cross Attention in Vision Transformer This is official implement of "CAT: Cross Attention in Vision Transformer". Abstract Since Transformer has

null 100 Dec 15, 2022
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting”

RGBT Crowd Counting Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin. "Cross-Modal Collaborative Representation Learning and a L

null 37 Dec 8, 2022
A Marvelous ChatBot implement using PyTorch.

PyTorch Marvelous ChatBot [Update] it's 2019 now, previously model can not catch up state-of-art now. So we just move towards the future a transformer

JinTian 223 Oct 18, 2022
Implement A3C for Mujoco gym envs

pytorch-a3c-mujoco Disclaimer: my implementation right now is unstable (you ca refer to the learning curve below), I'm not sure if it's my problems. A

Andrew 70 Dec 12, 2022
Perfect implement. Model shared. x0.5 (Top1:60.646) and 1.0x (Top1:69.402).

Shufflenet-v2-Pytorch Introduction This is a Pytorch implementation of faceplusplus's ShuffleNet-v2. For details, please read the following papers:

null 423 Dec 7, 2022