Browse > Article

Target Advertisement based on a TV Viewer's Profile Inference and TV Anytime Metadata  

Kim, Mun-Jo (한국정보통신대학교 공학부)
Lee, Bum-Sik (한국정보통신대학교 공학부)
Lim, Jeong-Yon (한국정보통신대학교 공학부)
Kim, Mun-Churl (한국전자통신연구원 방송미디어연구그룹 디지털방송연구단)
Lee, Hee-Kyung (한국전자통신연구원 방송미디어연구그룹 디지털방송연구단)
Lee, Han-Gyu (한국전자통신연구원 방송미디어연구그룹 디지털방송연구단)
Publication Information
Journal of KIISE:Computer Systems and Theory / v.33, no.10, 2006 , pp. 709-721 More about this Journal
Abstract
The traditional broadcasting services over terrestrial, satellite and cable media have been unidirectional mass media regardless of TV viewer's preferences. Recently ich media streaming has become possible via the broadb and networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming services has been emerging by taking into account the user's preference on content genres, viewing times and actors/actresses etc. Accordingly, personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for target advertisement which is considered an important application in personalcasting service. The proposed user profile reasoning method predicts an unknown TV viewer's gender and ages by analyzing TV Viewing history data. Based on the estimated user's gender and ages, a target advertisement is provided with TV Anytime metadata. A proposed target advertisement system is developed based on the user profile reasoning and the target advertisement selection method. To show the effectiveness of our proposed methods, we present a plenty of experimental results by using realistic TV viewing history data.
Keywords
target advertisement; multi-stage classifier; TV Anytime; profile inference;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Korea Broadcasting Advertising Corporation, 'Media & Consumer Research 2004 -소비자행태조사-,' http://www .kobaco.co.kr/kor/information/studydata/
2 Katsaros D. and Manolopoulos Y., 'Broadcast program generation for webcasting,' Source Data & Knowledge Engineering, Volume 49, Issue 1, pp. 1-21, April 2004   DOI   ScienceOn
3 Bozios T., Lekakos G., Skoularidou V. and Chorianopoulos, K., 'Advanced Techniques for Personalised in a Digital TV Environment: The iMEDIA System,' Proceedings of the E-business and E-work Conference, 17-19 October, Venice, Italy, pp. 1025-1031, 2001
4 Miyahara K. and Pazzani M. J., 'Collaborative filtering with the simple bayesian classifier,' Proceedings of the Sixth Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000
5 Shahabi C., Faisal A., Kashani F. B. and Faruque J., 'INSITE: A Tool for interpreting Users,' Proceeding of Interaction with a Web Space. Volume00, pp. 635-638
6 Yu Z., Zhou X., 'TV3P: an adaptive assistant for personalized TV,' Journal of IEEE Transactions on Consumer Electronics, vol. 50, Issue 1, pp. 393-399, Feb 2004   DOI   ScienceOn
7 Yuan W .. Liu J., and Zhou H. B., 'An Improved KNN Method and Its Application To Tumor Diagnosis,' Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, August 2004   DOI
8 TV Anytime Forum, 'Metadata,' S-3v1.3, Part-A: Metadata Schemas, Dec. 2002
9 ISO/lEC JTC1/SC29 WG11 (MPEG), 'MPEG-7 Multimedia Description Schemes,' ISO/lEC 15938-5 FDIS, N4242/MPEG57, Sydney, Australia, July 2001
10 TV Anytime Forum, 'Content Referencing,' S-4v1.2 June 2002