Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

Last update: Nov 12, 2022

Related tags

Recommender Systems SR-HGNN

Overview

SR-HGNN

ICDM-2020 《Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks》

Environments

python 3.8
pytorch-1.6
DGL 0.5.3 (https://github.com/dmlc/dgl)

Example to run the codes

dataprocess:

python dataProcess.py --dataset [CiaoDVD,Epinions,Douban] --rate [0.8,0.6]

train SR-HGNN model:

python main.py --dataset CiaoDVD --rate 0.8 --batch 128 --lr 0.001 --layer [16,16] --r 0.001 --r2 0.2 --r3 0.01 --dgi_hide_dim 500 --dgi_lr 0.001 --lam_t 0.05 --lam_r 0.1

Owner

xhc

GitHub Repository

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Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

Related tags

Overview

SR-HGNN

Environments

Example to run the codes

Owner

xhc

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

A library of Recommender Systems

Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch

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

Price-aware Recommendation with Graph Convolutional Networks,

A library of metrics for evaluating recommender systems

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".

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