Research Interests

Music Recommender Systems
Data Mining


Kibeom Lee, Sangmin Lee and Kyogu Lee, “In Search of User Features for Identifying Different Inspection Behaviors on Recommended Items”, Proceedings of KDD 2015 Workshop on Interactive Data Exploration and Analytics (IDEA’15), pp. 56-62, Sydney, Australia, 2015.
K. Lee, K. Lee, Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items, Expert Systems with Applications, Volume 42, Issue 10, 15 June 2015, Pages 4851-4858, ISSN 0957-4174,
Z. Hyung, K. Lee, K. Lee, Music Recommendation Using Text Analysis on Song Requests to Radio Stations, Expert Systems with Applications, Volume 41, Issue 5, April 2014, Pages 2608-2618, ISSN 0957-4174,
K. Lee and K. Lee. Using Dynamically Promoted Experts for Music Recommendation. Multimedia, IEEE Transactions on, 16(5):1-10, 2014.
K. Lee and K. Lee. Using Experts Among Users for Novel Movie Recommendations. JCSE, 7(1):21-29, 2013.
K. Lee and K. Lee. My head is your tail: Applying Link Analysis on Long-tailed Music Listening Behavior for Music Recommendation. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys’11, pages 213-220, New York, NY, USA, 2011. ACM.
K. Lee, W. S. Yeo, and K. Lee. Music Recommendation in the personal long tail: Using a Social-based Analysis of a User’s Long-tailed Listening Behavior. In Proceedings of the Workshop on Music Recommendation and Discovery, pages 47-54, 2010.


Music Recommender Systems