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

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

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

Self-supervised Graph Learning for Recommendation

Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation

Code for ICML2019 Paper "Compositional Invariance Constraints for Graph Embeddings"

Movies/TV Recommender

Price-aware Recommendation with Graph Convolutional Networks,

Respiratory Health Recommendation System

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

基于个性化推荐的音乐播放系统

This library intends to be a reference for recommendation engines in Python

Spotify API Recommnder System

Recommendation Systems for IBM Watson Studio platform

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

Recommendation System to recommend top books from the dataset

A movie recommender which recommends the movies belonging to the genre that user has liked the most.

reXmeX is recommender system evaluation metric library.

Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".

Graph Neural Network based Social Recommendation Model. SIGIR2019.