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

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

An Efficient and Effective Framework for Session-based Social Recommendation

Self-supervised Graph Learning for Recommendation

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

Recommendation System to recommend top books from the dataset

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

It is a movie recommender web application which is developed using the Python.

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

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

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

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.

Graph Neural Network based Social Recommendation Model. SIGIR2019.

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

A framework for large scale recommendation algorithms.

Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

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

fastFM: A Library for Factorization Machines

Spotify API Recommnder System