Earticle

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

키워드

저자

• Fulian Yin [ Faculty of science and technology, Communication University of China, Beijing, China ]
• Xingyi Pan [ Faculty of science and technology, Communication University of China, Beijing, China ]
• Jianping Chai [ Faculty of science and technology, Communication University of China, Beijing, China ]
• Wenwen Zhang [ Beijing Aerospace System Engineering Institute, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605