This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

Last update: Dec 05, 2022

Overview

GHCF

This is our implementation of the paper:

Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu and Shaoping Ma. 2021. Graph Heterogeneous Multi-Relational Recommendation. In AAAI'21.

Please cite our AAAI'21 paper if you use our codes. Thanks!

@inproceedings{chen2021graph,
  title={Graph Heterogeneous Multi-Relational Recommendation},
  author={Chen, Chong and Ma, Weizhi and Zhang, Min and Wang, Zhaowei and He, Xiuqiang and Wang, Chenyang and Liu, Yiqun and Ma, Shaoping},
  booktitle={Proceedings of AAAI},
  year={2021},
}

Example to run the codes

Train and evaluate our model:

python GHCF.py

Reproducibility

parser.add_argument('--wid', nargs='?', default='[0.1,0.1,0.1]',
                        help='negative weight, [0.1,0.1,0.1] for beibei, [0.01,0.01,0.01] for taobao')
parser.add_argument('--decay', type=float, default=10,
                        help='Regularization, 10 for beibei, 0.01 for taobao')
parser.add_argument('--coefficient', nargs='?', default='[0.0/6, 5.0/6, 1.0/6]',
                        help='Regularization, [0.0/6, 5.0/6, 1.0/6] for beibei, [1.0/6, 4.0/6, 1.0/6] for taobao')
parser.add_argument('--mess_dropout', nargs='?', default='[0.2]',
                        help='Keep probability w.r.t. message dropout, 0.2 for beibei and taobao')

Suggestions for parameters

Several important parameters need to be tuned for different datasets, which are:

parser.add_argument('--wid', nargs='?', default='[0.1,0.1,0.1]',
                        help='negative weight, [0.1,0.1,0.1] for beibei, [0.01,0.01,0.01] for taobao')
parser.add_argument('--decay', type=float, default=10,
                        help='Regularization, 10 for beibei, 0.01 for taobao')
parser.add_argument('--coefficient', nargs='?', default='[0.0/6, 5.0/6, 1.0/6]',
                        help='Regularization, [0.0/6, 5.0/6, 1.0/6] for beibei, [1.0/6, 4.0/6, 1.0/6] for taobao')
parser.add_argument('--mess_dropout', nargs='?', default='[0.2]',
                        help='Keep probability w.r.t. message dropout, 0.2 for beibei and taobao')

Specifically, we suggest to tune "wid" among [0.001,0.005,0.01,0.02,0.05,0.1,0.2,0.5]. It's also acceptable to simply make the three weights the same, e.g., self.weight = [0.1, 0.1, 0.1] or self.weight = [0.01, 0.01, 0.01]. Generally, this parameter is related to the sparsity of dataset. If the dataset is more sparse, then a small value of negative_weight may lead to a better performance.

The coefficient parameter determines the importance of different tasks in multi-task learning. In our datasets, there are three loss coefﬁcients λ 1 , λ 2 , and λ 3 . As λ 1 + λ 2 + λ 3 = 1, when λ 1 and λ 2 are given, the value of λ 3 is determined. We suggest to tune the three coefﬁcients in [0, 1/6, 2/6, 3/6, 4/6, 5/6, 1].

This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

Related tags

Overview

GHCF

Example to run the codes

Reproducibility

Suggestions for parameters

Owner

Chong Chen

Pytorch domain library for recommendation systems

QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Recommendation Systems for IBM Watson Studio platform

The implementation of the submitted paper "Deep Multi-Behaviors Graph Network for Voucher Redemption Rate Prediction" in SIGKDD 2021 Applied Data Science Track.

A TensorFlow recommendation algorithm and framework in Python.

Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021

Fast Python Collaborative Filtering for Implicit Feedback Datasets

This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

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

Graph Neural Network based Social Recommendation Model. SIGIR2019.

E-Commerce recommender demo with real-time data and a graph database

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

Cross-Domain Recommendation via Preference Propagation GraphNet.

Code for KHGT model, AAAI2021

Codes for AAAI'21 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks