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

Implementation of a hadoop based movie recommendation system

Pytorch domain library for recommendation systems

Movies/TV Recommender

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

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

Movie Recommender System

6002project-rl - An implemention of offline RL on recommender system

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs.

A Python scikit for building and analyzing recommender systems

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

Bundle Graph Convolutional Network

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020

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

Real time recommendation playground

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.

Plex-recommender - Get movie recommendations based on your current PleX library