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

RecList is an open source library providing behavioral, "black-box" testing for recommender systems.

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

Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions

A library of Recommender Systems

Bert4rec for news Recommendation

This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) (TKDE, 2021).

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

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

Recommendation Systems for IBM Watson Studio platform

Respiratory Health Recommendation System

Cloud-based recommendation system

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Movie Recommender System

Graph Neural Networks for Recommender Systems

Code for KHGT model, AAAI2021

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Attentive Social Recommendation: Towards User And Item Diversities

A tensorflow implementation of the RecoGCN model in a CIKM'19 paper, titled with "Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation".

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

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