ICDM-2020 《Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks》
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
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Books-Recommendation Business Problem During the last few decades, with the rise
Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021
Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various poin
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MB-GMN Code for MB-GMN, SIGIR 2021 For Beibei data, run python .\labcode.py For Tmall data, run python .\labcode.py --data tmall --rank 2 For IJCAI
Bundle Graph Convolutional Network This is our Pytorch implementation for the paper: Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin and Yong Li. Bun
concierge A continuous learning collaborative filter1 deployed with a light web server2. Distributed updates are live (real time pubsub + delta traini
Respiratory-Health-Recommendation-System Respiratory Health Recommendation System based on Air Quality Index Forecasts This project aims to provide pr
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