Hong Joo Lee
KAIST
66 Papers
204 Citations
Hong Joo Lee is an academic researcher from KAIST. The author has contributed to research in topics: Computer science & Personalization. The author has an hindex of 12, co-authored 61 publications. Previous affiliations of Hong Joo Lee include Massachusetts Institute of Technology & Catholic University of Korea.
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Papers
Use of social network information to enhance collaborative filtering performance
Fengkun Liu,Hong Joo Lee +1 more
TL;DR: This study developed a way to increase recommendation effectiveness by incorporating social network information into collaborative filtering, and indicated that more accurate prediction algorithms can be produced by incorporates social network Information into CF.
356
Utilizing knowledge context in virtual collaborative work
Hyung Jun Ahn,Hong Joo Lee,Kyehyun Cho,Sung Joo Park +3 more
- 01 Jun 2005
TL;DR: A knowledge context model, called KC-V, is presented, which facilitates the use of contextual information in virtual collaborative work and four benefits are suggested: evolutionary accumulation of knowledge aligned with collaborative activities, supporting the virtual team lifecycle, improved understanding by rich navigation paths, and searching for knowledge with similar context.
156
MONERS: A news recommender for the mobile web
Hong Joo Lee,Sung Joo Park +1 more
TL;DR: Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.
124
Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary
Hong Joo Lee,Jung Uk Kim,Sangmin Lee,Hak Gu Kim,Yong Man Ro +4 more
- 14 Jun 2020
TL;DR: A novel image segmentation method to tackle two critical problems of medical image, which are ambiguity of structure boundary in the medical image domain and uncertainty of the segmented region without specialized domain knowledge is proposed.
The influence of national culture on the attitude towards mobile recommender systems
TL;DR: Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social as discussed by the authors, and the relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance.
78