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

[ CV  /  Google Scholar  /  DBLP  /  LinkedIn ]

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Research Interests
    My research aims to enhance recommender systems by employing cutting-edge AI techniques, prioritizing critical challenges beyond accuracy, such as diversity, calibration, and fairness. Currently, I am developing fair and effective conversational recommender systems (CRS) using Large Language Models (LLMs), aiming to deliver equitable and precise recommendations across diverse user groups.
News
  • [Jul. 2024] Our work on conversational recommendation has been accepted for the short paper track at RecSys 2024. It has been an invaluable experience collaborating with the amazing team at Netflix Research!
  • [Jul. 2024] Our work on calibrated sequential recommendation has been accepted at CIKM 2024.
  • [Jan. 2024] Our work on cold-start bundle recommendation has been 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 have been accepted at PAKDD 2023.
  • [Jan. 2023] Our work on bundle recommendation has been accepted at PLOS ONE.
Position
  • University of California San Diego, California, United States
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    Education
  • Seoul National University, Seoul, South Korea
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  • Hanyang University, Seoul, South Korea
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    Experience
  • Hyperconnect, Seoul, South Korea
    • Research Intern, Machine Learning Team (Jul. 2020 - Aug. 2020)
    Awards and Honors
    Publications
    2024
    clean-usnob Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation
    Hyunsik Jeon, Se-eun Yoon, and Julian McAuley
    CIKM 2024, Boise, Idaho, USA
    [paper / slides / code / bib]
    TL;DR
    How can we reflect a balanced representation of user interests in sequential recommendations? We propose LeapRec which effectively combines model training with reranking to address the challenges of calibrating recommendations in a dynamic user preference environment.
    clean-usnob Neighborhood-Based Collaborative Filtering for Conversational Recommendation
    Zhouhang Xie*, Junda Wu*, Hyunsik Jeon*, Zhankui He, Harald Steck, Rahul Jha, Dawen Liang, Nathan Kallus, and Julian McAuley
    RecSys 2024 - Short Paper, Bari, Italy
    [paper / poster / code / bib]
    TL;DR
    How can we address conversational recommendations without relying on cumbersome external knowledge or extensive training of large language models? We propose NBCRS which utilizes neighborhood-based methods to provide effective and efficient conversational recommendations.
    clean-usnob 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 / slides / poster / code / bib]
    TL;DR
    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.
    2023
    clean-usnob 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]
    TL;DR
    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.
    clean-usnob 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]
    TL;DR
    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.
    clean-usnob 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]
    TL;DR
    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.
    2022
    clean-usnob 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]
    TL;DR
    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]
    TL;DR
    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.
    2021
    clean-usnob PGT: News Recommendation Coalescing Personal and Global Temporal Preferences
    Bonhun Koo, Hyunsik Jeon, and U Kang
    KAIS (2021)
    [paper / bib]
    TL;DR
    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]
    TL;DR
    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]
    TL;DR
    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.
    2020 and Before
    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]
    TL;DR
    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]
    TL;DR
    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]
    TL;DR
    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.
    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]
    TL;DR
    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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