Earticle

초록

영어

Increasing amounts of TV contents are being disseminated through advanced broadcast, satellite and smart TV technologies leading to an information overload. Despite the excessive increase of TV contents, there are few considerations in the development of novel methods for personalized smart TV content recommender services. Most existing personalized TV content services are mainly focus on using individual profiles and require user’s immediate participation in rating their experienced contents. In this paper, we propose the context tagging-based user’s preference prediction mechanism by extending the widely known recommender algorithm, collaborative filtering (CF) in order to increase the user’s satisfaction about the recommender service. The development of the prototype system shows the usefulness of proposed mechanism. And the experiment confirms that the proposed mechanisms improve the recommender system.

목차

Abstract
 1. Introduction
 2. Related Works and Background
  2.1 TV contents recommender systems
  2.2 Context awareness for personalization
 3. Preference Prediction based on Collaborative Context Tagging
  3.1. Collaborative context tagging model
  3.2. Collaborative context awareness-based viewer’s preference prediction
 4. Prototype of Personalized TV Content Recommender Services
 5. Experiment
 6. Conclusions and Future Works
 Acknowledgement
 References

키워드

저자

• Haesung Lee [ Department of Computer Science, Kyonggi University ]
• Joonhee Kwon [ Department of Computer Science, Kyonggi University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605