Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Last update: Nov 18, 2022

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Recommender Systems KCGN

Overview

KCGN

AAAI-2021 《Knowledge-aware Coupled Graph Neural Network for Social Recommendation》

Environments

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

Example to run the codes

train model:

Dataset: Yelp, Result: test HR = 0.8026, test NDCG = 0.5326
python main.py --dataset Yelp --reg 0.05 --lr 0.01 --batch 2048 --hide_dim 64 --layer [64] --slope 0.4 --time_step 360 --lam [0.1,0.001] --clear 1 --fuse mean

Owner

xhc

GitHub Repository

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Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Related tags

Overview

KCGN

Environments

Example to run the codes

Owner

xhc

A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

Cross-Domain Recommendation via Preference Propagation GraphNet.

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Mutual Fund Recommender System. Tailor for fund transactions.

A framework for large scale recommendation algorithms.

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

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Books Recommendation With Python

Movie Recommender System

Movies/TV Recommender

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

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