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이연창
Lee, Yeon-Chang

이연창

Graduate School of Artificial Intelligence(인공지능대학원)

Lab Description
Data Intelligence Lab focuses on collecting, analyzing, and utilizing diverse and complex real-world data effectively. Our major research topics include data mining, graph machine learning, network science, and recommendation systems. Our goal is to develop "accurate and trustworthy data mining methodologies," which can help solve real-world problems and enhance the quality of life in society.
Researcher ID Research Gate ORCiD Scopus Google Scholar
Previous
Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion File

Lee, Yeon-Chang , Lee, Jaehyun , Lee, Dongwon , Kim, Sang-Wook

Article Issue Date2024-01 View10
Uninteresting Items: Concept and Its Application to Effective Collaborative Filtering in Recommender Systems File

Lee, Yeon-Chang , Kim, Sang-Wook

Article Issue Date2023-12 View13
A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning File

Kang, David Y. , Lee, Woncheol , Lee, Yeon-Chang , Han, Kyungsik , Kim, Sang-Wook

Article Issue Date2023-11 View12
Effective and efficient negative sampling in metric learning based recommendation File

Park, Junha , Lee, Yeon-Chang , Kim, Sang-Wook

Article Issue Date2022-08 View10
Exploiting uninteresting items for effective graph-based one-class collaborative filtering File

Lee, Yeon-Chang , Son, Jiwon , Kim, Taeho , Park, Daeyoung , Kim, Sang-Wook

Article Issue Date2021-07 View12
암시적 피드백을 다루는 협업 필터링 기반 추천 방법들의 최신 동향

김태호 , Lee, Yeon-Chang , 김상욱

Article Issue Date2021-03 View12
M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences File

Lee, Yeon-Chang , Kim, Taeho , Choi, Jaeho , He, Xiangnan , Kim, Sang-Wook

Article Issue Date2021-02 View15
CrowdStart: Warming up cold-start items using crowdsourcing File

Hong, Dong-Gyun , Lee, Yeon-Chang , Lee, Jongwuk , Kim, Sang-Wook

Article Issue Date2019-12 View12
Improving the accuracy of top-N recommendation using a preference model File

Lee, Jongwuk , Lee, Dongwon , Lee, Yeon-Chang , Hwang, Won-Seok , Kim, Sang-Wook

Article Issue Date2016-06 View9
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