zsjie99

ChatGLM2-6B

ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型

DeepCTR-Torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

DGCN

The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)

DGRec

DTDBD

GF_CF

GraphHINGE

Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

LightGCN-PyTorch

The PyTorch implementation of LightGCN

MCLRec

[SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".

NCL

[WWW'22] Official PyTorch implementation for "Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning".

zsjie99/ChatGLM2-6B

ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型

zsjie99/DeepCTR-Torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

zsjie99/DGCN

The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)

zsjie99/DGRec

zsjie99/DTDBD

zsjie99/GF_CF

zsjie99/GraphHINGE

Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

zsjie99/LightGCN-PyTorch

The PyTorch implementation of LightGCN

zsjie99/MCLRec

[SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".

zsjie99/NCL

[WWW'22] Official PyTorch implementation for "Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning".

zsjie99/RecBole

A unified, comprehensive and efficient recommendation library

zsjie99/RecBole-GNN

Efficient and extensible GNNs enhanced recommender library based on RecBole.

zsjie99/RecZoo

A curated model zoo for recommendation tasks

zsjie99/SELFRec

An open-source framework for self-supervised recommender systems.

zsjie99/SGL_reproduction

PyTorch Implementation of "Self-supervised Graph Learning for Recommendation"