Hyunsik Jeon
Ph.D. student at Seoul National University

I am a Ph.D student of Data Mining Lab at Seoul National University, where I am fortunate to be advised by Prof. U Kang. I received my B.S. in Computer Science and Engineering at Hanyang University. My research interests include recommender systems for various practical scenarios, such as sequential recommendation, news recommendation, IoT recommendation, and bundle recommendation.

Email: jeon185 at gmail.com

[ CV  /  Google Scholar  /  DBLP  /  LinkedIn ]

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News
  • [Aug. 2022] I won SIGIR Student Travel Grant for CIKM 2022 attendance.
  • [Aug. 2022] Our work on IoT recommendation was accepted to CIKM 2022.
  • [May. 2022] Our work on feature estimation in graphs was accepted to KDD 2022.
  • [Oct. 2021] Our extending work on news recommendation was accepted to KAIS.
  • [Jul. 2021] Our work on multi-domain adaptation was accepted to PLOS ONE.
  • [Jun. 2021] Our work on multi-domain adaptation was accepted to PLOS ONE.
  • [Sep. 2020] We won the second prize award at MIND News Recommendation Competition, among 215 teams
  • [May. 2020] Our paper "Accurate News Recommendation Coalescing Personal and Global Temporal Preferences" won the best student award at PAKDD 2020.
  • [Feb. 2020] I won the Samsung HumanTech paper award, honorable mention (4th in CSE).
  • [Jan. 2020] Our work on news recommendation was accepted to PAKDD 2020.
  • [Nov. 2019[ I won Student Travel Award at IEEE BigData 2019.
  • [Oct. 2019] Our work on recommendation with additional information was accepted to IEEE BigData 2019.
  • [May. 2019] Our work on node classification in graphs was accepted to IJCAI 2019.
Education
Work Experience
  • Hyperconnect, Seoul, South Korea
  • Research Intern, Machine Learning team (Jul. 2020 - Aug. 2020)
Awards and Honors
Research Interests

I am highly interested in recommender systems for valuable services. I have studied sequential recommendation, news recommendation, IoT recommendation, and bundle recommendation. In addition, I am fascinated by researching recommender systems that model user behaviors in real-world services and improve user experiences.

Publications
clean-usnob Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
Hyunsik Jeon, Jongjin Lee, Hoyoung Yoon, Jaeri Lee, and U Kang
CIKM 2022, Atlanta, Georgia, USA
SIGIR Student Travel Grant
[paper / dataset / bib ]

How can we accurately recommend actions for users to control their devices at home? We propose SmartSense which recommends device controls to users, accurately.

clean-usnob Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang
KDD 2022, Washington D.C., USA
[paper / slides / code / bib ]

Given a graph with partial observations of node features, how can we estimate the missing features accurately? We propose SVGA which estimates the missing features of nodes, accurately.

clean-usnob PGT: News Recommendation Coalescing Personal and Global Temporal Preferences
Bonhun Koo, Hyunsik Jeon, and U Kang
KAIS (2021)
[paper / bib]

Given sequential news watch logs of users, how can we accurately recommend news articles? We propose PGT which recommends personalized news to the users, accurately.

clean-usnob Multi-EPL: Accurate multi-source domain adaptation
Seongmin Lee, Hyunsik Jeon, and U Kang
PLOS ONE (2021)
[paper / code / bib]

Given multiple source datasets with labels, how can we train a target model with no labeled data? We propose Multi-EPL which accurately classifies the target dataset while utilizing multiple source datasets.

clean-usnob Unsupervised Multi-Source Domain Adaptation with No Observable Source Data
Hyunsik Jeon, Seongmin Lee, and U Kang
PLOS ONE (2021)
[paper / code / bib]

Given trained models from multiple source domains without any data, how can we predict the labels of unlabeled data in a target domain? We propose DEMS which adapts target data to source domains and accurately estimates the target labels, without exploiting any source data.

clean-usnob Accurate News Recommendation Coalescing Personal and Global Temporal Preferences
Bonhun Koo, Hyunsik Jeon, and U Kang
PAKDD 2020, Singapore
Best Student Paper Award
[paper / homepage / bib]

Given session-based watch history of users, how can we precisely recommend new articles? We propose PGT which recommends news to the users, accurately.

clean-usnob Data Context Adaptation for Accurate Recommendation with Additional Information
Hyunsik Jeon, Bonhun Koo, and U Kang
BigData 2019, Los Angeles, USA
Samsung HumanTech Paper Award, BigData Student Travel Grant
[paper / slides / homepage / bib]

Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? We propose DaConA which accurately predicts ratings utilizing auxiliary matrix, based on neural networks.

clean-usnob Belief Propagation Network for Hard Inductive Semi-supervised Learning
Jaemin Yoo, Hyunsik Jeon, and U Kang
IJCAI 2019, Macao, China
[paper / slides / poster / code / bib]

Given graph-structured data, how can we train a robust classifier in a semi-supervised setting that performs well without neighborhood information? We propose BPN which accurately classifies nodes in a hard inductive setting, based on a neural network.

Preprints
clean-usnob UniWalk: Explainable and Accurate Recommendation for Rating and Network Data
Haekyu Park, Hyunsik Jeon, Junghwan Kim, Beunguk Ahn, and U Kang
arXiv (2017)
[arxiv / homepage / bib ]

Given a social network and ratings, how can we correctly recommend proper items and provide a persuasive explanation for the recommendation? We propose UniWalk which accurately recommends items and explains the reasons while exploiting both social network and rating data.

Professional Services

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