Prof.
Hong-Kyun Bae
배홍균 교수
Personalized Recommender Systems (PreCS) Lab.
Contact
Phone 02-910-5652
E-mail hongkyun@kookmin.ac.kr
E-mail hongkyun@kookmin.ac.kr
Education
학력
Ph.D. Hanyang University, Computer Science (2024)
B.S. Inha University, Computer Science (2016)
B.S. Inha University, Computer Science (2016)
Career
경력
2025-Present Assistant Professor, Dept. of Computer Science, Kookmin University
2024-2025 Postdoctoral Researcher, Hanyang University
2023 Visiting Researcher, Penn State University
2024-2025 Postdoctoral Researcher, Hanyang University
2023 Visiting Researcher, Penn State University
2025-현재 국민대학교 컴퓨터공학부 조교수
2024-2025 한양대학교 박사후연구원
2023 Penn State University 방문연구원
2024-2025 한양대학교 박사후연구원
2023 Penn State University 방문연구원
Research Overview 연구 개요
Research focuses on recommender systems, data mining, and artificial intelligence. Key areas include ranking optimization, negative sampling techniques, lifetime-aware recommendation strategies, and knowledge distillation for recommendation.
추천 시스템, 데이터 마이닝, 인공지능 분야를 중심으로 연구하고 있습니다. 주요 연구 주제로는 랭킹 최적화, 부정 샘플링 기법, 수명 인식 추천 전략, 추천을 위한 지식 증류 등이 있습니다.
Research Areas 연구 분야
- Recommender Systems
- Data Mining
- Learning-to-Rank
- Knowledge Distillation
- News Recommendation
- 추천 시스템
- 데이터 마이닝
- 랭킹 학습
- 지식 증류
- 뉴스 추천
Major Achievements 주요 연구 성과
- Ranking items by current-preferences and profits (ACM WWW 2025)
- LANCER: Lifetime-aware news recommender system (AAAI 2023)
- DUET: Dually guided knowledge distillation (Information Fusion 2025)
Honors & Awards 수상
- Outstanding Ph.D. Dissertation Award, Hanyang University, 2024
Recent Publications 주요 논문
- H.-K. Bae, H.-R. Jang, W.-Y. Shin, and S.-W. Kim, "Ranking Items by the Current-Preferences and Profits: A List-wise Learning-to-Rank Approach to Profit Maximization," ACM WWW, 2025
- H.-K. Bae, J. Kim, J. Lee, and S.-W. Kim, "DUET: Dually Guided Knowledge Distillation from Explicit Feedback," Information Fusion, 2025
- H.-K. Bae, Y. Kim, H. Kim, and S.-W. Kim, "Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation," ACM WWW, 2024
- H.-K. Bae, H.-R. Jang, Y.-S. Moon, and S.-W. Kim, "Item-Ranking Promotion in Recommender Systems," ACM WWW (Short), 2024
- J. Ahn, H.-K. Bae, and S.-W. Kim, "Is the Impression Log Beneficial to Evaluating News Recommender Systems? No, it is Not!," ACM WWW (Short), 2024
- H.-K. Bae, Y.-C. Lee, K. Han, and S.-W. Kim, "A Competition-Aware Approach to Accurate TV Show Recommendation," IEEE ICDE, 2023
- H.-K. Bae, J. Ahn, D. Lee, and S.-W. Kim, "LANCER: A Lifetime-Aware News Recommender System," AAAI, 2023
- J. Ahn, H.-K. Bae, and S.-W. Kim, "Is the Impression Log Beneficial to Effective Model Training in News Recommender Systems? No, It's NOT," ACM WWW (Short), 2023