/2021-summer-internship

2021 summer internship program at DSAIL

2021-summer-internship

Kaist Summer Program

drawing

Data Science and Artificial Intelligence Laboratory (DSAIL)

Tentative Schedule

Basic recommender system and Graph Neural Network

Year Paper Short
2009 Matrix Factorization Techniques for Recommender Systems Netflix
2008 Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model Netflix
2008 Probabilistic Matrix Factorization PMF
2008 Collaborative Filtering for Implicit Feedback Datasets OCCF
2012 BPR: Bayesian Personalized Ranking from Implicit Feedback BPR
2017 Collaborative Metric Learning CML
2017 Neural Collaborative Filtering NCF
2015 AutoRec: Autoencoders Meet Collaborative Filtering Autorec
2015 Collaborative Deep Learning for Recommender Systems CDL
2010 Factorization Machines FM
2016 Wide & Deep Learning for Recommender Systems WD
2008 SoRec: Social Recommendation Using Probabilistic Matrix Factorization SoRec
2011 Recommender Systems with Social Regularization SoReg
2016 Deep Neural Networks for YouTube Recommendations Youtube
2014 DeepWalk: Online Learning of Social Representations Deepwalk
2016 Node2vec : Scalable Feature Learning for Networks Node2Vec
2016 LINE : Large-scale Information Network Embedding LINE
2017 metapath2vec : Scalable Representation Learning for Heterogeneous Networks Metapath2Vec
2016 Semi-Supervised Classification with Graph Convolutional Networks GCN
2017 Graph Attention Network GAT
2018 Deep Graph Infomax DGI
2016 Variational Graph Auto-Encoders VGAE
2013 Auto-Encoding Variational Bayes VAE
2017 Inductive Representation Learning on Large Graphs GraphSAGE
2013 Translating Embeddings for Modeling Multi-relational Data TransE

Participate to projects

  • On-going projects
    • 인공지능 기반 보험금 지급 예측
    • 검색 시스템을 위한 지식그래프 구축
    • 소재의 화학 구조로부터 물성 예측
    • 상품분류체게 자동 구축
  • 각 프로젝트당 2명 배정 (담당 대학원생과 함께 진행)

Team Introduction (M.S. Student & Lab Interns)

인연준

김정훈

주혜민

정지형

이한빛

이재영

김수진

김성원

김효준