Hyunsik Jeon
Postoctoral Researcher at UC San Diego
I am currently a Postdoctoral Researcher in the Dept. of Computer Science and Engineering at University of California San Diego, where I am working with Prof. Julian McAuley.
I received my Ph.D. and M.Sc. from the Dept. of Computer Science and Engineering at Seoul National University, where I was advised by Prof. U Kang.
I obtained my B.Sc. from the Dept. of Computer Science and Engineering at Hanyang University.
My research interests lie in enhancing recommender systems, with a focus on more accurate, fair, and interactive recommendation solutions.
Email: hyjeon (at) ucsd.edu, jeon185 (at) gmail.com
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Research Interests
My primary research focuses on enhancing recommender systems for various web services.
I am also broadly interested in data mining and applied machine learning.
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News
- [Jan. 2024] Our work on cold-start bundle recommendation was accepted at The Web Conference 2024.
- [Aug. 2023] I successfully defended my PhD thesis and received the Distinguished PhD Dissertation Award from Seoul National University.
- [May. 2023] I was honored to receive the Sejong Science Fellowship Grants under the Overseas Training Track. I plan to extend my research into the development of robust and interactive recommender systems.
- [Feb. 2023] Our two works on diversified recommendation were accepted at PAKDD 2023.
- [Jan. 2023] Our work on bundle recommendation was accepted at PLOS ONE.
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Position
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University of California San Diego, California, United States
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Education
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Seoul National University, Seoul, South Korea
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Hanyang University, Seoul, South Korea
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Experience
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Hyperconnect, Seoul, South Korea
Research Intern, Machine Learning Team (Jul. 2020 - Aug. 2020)
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Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
Hyunsik Jeon, Jong-eun Lee, Jeongin Yun, and U Kang
WWW 2024, Singapore
Oral Presentation
[paper / code / bib]
How can we accurately recommend cold-start bundles to users? We propose CoHeat which recommends cold-start bundles (as well as warm-start bundles), accurately.
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Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking
Hyunsik Jeon, Jongjin Kim, Jaeri Lee, Jong-eun Lee, and U Kang
PAKDD 2023, Osaka, Japan
[paper / slides / code / bib]
How can we expose diverse items across all users while satisfying their needs in bundle recommendations? We propose PopCon which recommends top-k bundles to users, accurately and aggregately diversely.
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Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation
Jongjin Kim, Hyunsik Jeon, Jaeri Lee, and U Kang
PAKDD 2023, Osaka, Japan
[paper / arxiv / code / bib]
When recommending personalized top-k items to users, how can we recommend the items diversely to them while satisfying their needs? We propose DivMF which recommends top-k items to users, accurately and aggregately diversely.
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Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters
Hyunsik Jeon, Jun-Gi Jang, Taehun Kim, and U Kang
PLOS ONE (2023)
[paper / arxiv / code / bib]
Given item and bundle purchase histories of users, how can we match existing bundles to the users and generate new bundles for them? We propose BundleMage which matches existing bundles and generates personalized bundles simultaneously.
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Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
Hyunsik Jeon, Jongjin Kim, Hoyoung Yoon, Jaeri Lee, and U Kang
CIKM 2022, Atlanta, Georgia, USA
SIGIR Student Travel Grant
[paper / slides / codes / 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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