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Description: “추천시스템 관련 링크”

Table of contents

  1. Engineers
  2. RecFra
  3. Rec Lib
  4. Rec paper Repo
  5. Dataset
  6. Tema
  7. News
  8. Lecture
  9. tutorial

Engineers

  1. pyxelr/recommendations-for-engineers

RecFra

  1. RUCAIBox/RecBole
  2. RUCAIBox/RecBole2.0
  3. microsoft/recommenders
  4. wubinzzu/NeuRec
  5. rn5l/session-rec
  6. MaurizioFD/RecSys_Course_AT_PoliMi
  7. beta-team/beta-recsys
  8. alonsoir/awesome-recommendation-engine
  9. maciejkula/spotlight
  10. cowjen01/repsys
  11. shenweichen/DeepCTR-Torch
  12. shenweichen/DeepCTR
  13. NVIDIA-Merlin/HugeCTR

Rec Lib

  1. yoonjong12/pinsage
  2. pytorch/torchrec
  3. ShinhwanKang/BeGin

Rec paper Repo

  1. FeiSun/BERT4Rec
  2. guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising
  3. loserChen/Awesome-Recommender-System
  4. SeongBeomLEE/RecsysTutorial
  5. bladejun/Recommend_System_Pytorch
  6. doublemul/ADER
  7. hongleizhang/RSPapers
  8. CRIPAC-DIG/TAGNN
  9. CRIPAC-DIG/SR-GNN
  10. gusye1234/LightGCN-PyTorch
  11. lyst/lightfm
  12. xiangwang1223/neural_graph_collaborative_filtering
  13. kuandeng/LightGCN
  14. zzaebok/KGCN-pytorch
  15. cn-uofbasel/PiCN
  16. AIM-SE/DIF-SR
  17. donalee/BUIR

Dataset

  1. otto-de/recsys-dataset
  2. khanhnamle1994/movielens
  3. sidooms/MovieTweetings

Tema

  1. kakao/recoteam
  2. Pseudo-Lab/Recommendation-System

News

  1. SeongBeomLEE/RecSys-Tech-Blog-Article

Lecture

  1. jaewonlee-728/fastcampus-RecSys

tutorial

  1. xei/recommender-system-tutorial
  2. fastforwardlabs/session_based_recommenders