cutefishh

Tri-CDR

The source code is for the paper: “Triple Sequence Learning for Cross-domain Recommendation” accepted in TOIS by Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin and Jie Zhou.

RecDCL

RecDCL: Dual Contrastive Learning for Recommendation (WWW'24, Oral)

DGR

ProtoAU

Pytorch implementation of ProtoAU for recomandation.

broken-link-checker

Find broken links, missing images, etc within your HTML.

linux-command

Linux命令大全搜索工具，内容包含Linux命令手册、详解、学习、搜集。https://git.io/linux

mysql-tutorial

MySQL入门教程（MySQL tutorial book）

html-differ

Сompares two HTML

DefinitelyTyped

The repository for high quality TypeScript type definitions.

generate-pdf

PDF 生成

blog

一个能让你升 P7 的仓库，框架源码原理分析（比如 Vue、微前端、组件库等）、业界最佳实践等

tiptap-extension-video

Tiptap 2 Extension for adding videos

tiptap-media-resize

Tiptap 2 Media 🌁 🎥 Resize, Realignment extension.

tiptap-comment-extension

Google-Docs 📄🔥 like commenting 💬 solution for Tiptap 2(https://tiptap.dev)

RGCF

[SIGIR'22] Official PyTorch implementation for "Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering".

Awesome-SSLRec-Papers

A Comprehensive Survey of Self-Supervised Learning for Recommendation

DisenHAN

Source code of DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation, CIKM 2020

DisenGCN

Simple implementation of two layers of Disentangled GCN

CDR

This is the repository of the NeurIPS 2021 paper "Curriculum Disentangled Recommendation withNoisy Multi-feedback"

MadGap

Releated Paper: (AAAI2020) Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View.

AdvDrop

AFDGCF

The code of our paper "AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations" accepted by SIGIR2024

LibMTL

A PyTorch Library for Multi-Task Learning

awesome-java

Collection of awesome Java project on Github(非常棒的 Java 开源项目集合).

GraphAug

[ICDE'2024] "GraphAug: Graph Augmentation for Recommendation"

KACL

iclr2024_learning_on_graphs

List of papers on ICLR 2024

AutoCF

[WWW'23] "AutoCF: Automated Self-Supervised Learning for Recommendation"

DA-CL-4Rec

The latest research progress of Contrastive Learning(CL), Data Augmentation(DA) and Self-Supervised Learning(SSL) in Recommender Systems

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.