원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.11
2016.11
pp.79-90
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper proposes an analysis method of user behavior to provide personalized program recommendation based on program tags in the field of broadcasting and television. Multidimensional Scaling Analysis is used to produce a quantitative description of viewing preferences. Hierarchical clustering is performed to determine the number of clusters, followed by K-means clustering to group the data according to audience interest in TV program tags. This divides the audience into groups with similar viewing preferences.
목차
Abstract
1. Introduction
2. Audience Interest based on Program Tags
2.1. Delayering Tagging System of Television Programs
2.2. Audience Interest of Program Tags
3. Analysis of Audience Interest
3.1. Personal Tag Cloud of Audience Interest
3.2. Audience Multidimensional Scaling based on AIT
4. Audience Crowd Clustering based on AIT
4.1. Audience Clustering
4.2. Hierarchical clustering of Audience Interest of TV Program Tags
4.3. K-Means Clustering of Audience Interest of TV Program Tag
4.4. Simulation and Performance Analysis
5. Conclusion
References
1. Introduction
2. Audience Interest based on Program Tags
2.1. Delayering Tagging System of Television Programs
2.2. Audience Interest of Program Tags
3. Analysis of Audience Interest
3.1. Personal Tag Cloud of Audience Interest
3.2. Audience Multidimensional Scaling based on AIT
4. Audience Crowd Clustering based on AIT
4.1. Audience Clustering
4.2. Hierarchical clustering of Audience Interest of TV Program Tags
4.3. K-Means Clustering of Audience Interest of TV Program Tag
4.4. Simulation and Performance Analysis
5. Conclusion
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보