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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.Sc. in Computer Science and Engineering at Hanyang University. My research interests lies on recommender systems for various practical scenarios, such as bundle recommendation, news recommendation, IoT recommendation, and bundle recommendation. Email: jeon185 at gmail.com [ CV / Google Scholar / DBLP / LinkedIn ] |
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Diversified recommendation (Arxiv'22) and explainable recommendation (Arxiv'17) Utilizing additional information (BigData'19) |
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M.Sc. in Computer Science and Engineering (Feb. 2019) |
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Technical report Press (in Korean) Press (in Korean) |
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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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Hyunsik Jeon, Jongjin Kim, Hoyoung Yoon, Jaeri Lee, and U Kang CIKM 2022, Atlanta, Georgia, USA SIGIR Student Travel Grant [paper / slides / 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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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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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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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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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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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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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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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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Jongjin Kim, Hyunsik Jeon, Jaeri Lee, and U Kang arXiv (2022) [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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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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