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zhaosiheng's Projects

bustub icon bustub

The BusTub Relational Database Management System (Educational)

cikm2020-s3rec icon cikm2020-s3rec

Code for CIKM2020 "S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization"

dhcn icon dhcn

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

dig icon dig

A library for graph deep learning research

disen-gnn icon disen-gnn

This is the code for the Paper: Disentangled Graph Neural Networks for Session-based Recommendation

gce-gnn icon gce-gnn

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

gce-gnn-1 icon gce-gnn-1

The paper author has released the source code as following https://github.com/CCIIPLab/GCE-GNN

gifify icon gifify

😻 Convert any video file to an optimized animated GIF.

graphtransformer icon graphtransformer

Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.

han icon han

Heterogeneous Graph Neural Network

heco icon heco

The source code of HeCo

proxysr icon proxysr

ProxySR (Unsupervised Proxy Selection for Session-based Recommender Systems, SIGIR'21)

pytorch-gat icon pytorch-gat

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

recbole icon recbole

A unified, comprehensive and efficient recommendation library

sasrec icon sasrec

SASRec: Self-Attentive Sequential Recommendation

sasrec.pytorch icon sasrec.pytorch

PyTorch(1.6+) implementation of https://github.com/kang205/SASRec

sedgn_code icon sedgn_code

python code for the paper "SEDGN: Sequence Enhanced Denoising Graph Neural Network for Session-based Recommendation"

selftask-gnn icon selftask-gnn

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

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