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Weight-Aware Multidimensional Advertising for TV Programs

Weight-Aware Multidimensional Advertising for TV Programs

Jianmin Wang, Yi Liu, Ting Xie, Yuchu Zuo
Copyright: © 2013 |Volume: 5 |Issue: 4 |Pages: 11
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781466635517|DOI: 10.4018/ijaci.2013100101
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MLA

Wang, Jianmin, et al. "Weight-Aware Multidimensional Advertising for TV Programs." IJACI vol.5, no.4 2013: pp.1-11. http://doi.org/10.4018/ijaci.2013100101

APA

Wang, J., Liu, Y., Xie, T., & Zuo, Y. (2013). Weight-Aware Multidimensional Advertising for TV Programs. International Journal of Ambient Computing and Intelligence (IJACI), 5(4), 1-11. http://doi.org/10.4018/ijaci.2013100101

Chicago

Wang, Jianmin, et al. "Weight-Aware Multidimensional Advertising for TV Programs," International Journal of Ambient Computing and Intelligence (IJACI) 5, no.4: 1-11. http://doi.org/10.4018/ijaci.2013100101

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Abstract

Given ongoing developments in the digital television industry, the consumption habits of consumers are substantially influenced by advertisements, which become the main revenue source for TV broadcasters. Therefore, the effective deployment of advertisements is necessary. Digital television is a thriving sector and the number of channels continues to increase, so that the various dimension information of data on electronic programming guides overwhelm the advertisement recommendation systems for TV programs. In this paper, considering the viewing scenarios the users viewed in the different types of television program, the authors present a weight-aware multidimensional model approach that focuses on the different weights of advertisement or program content parameters and their interrelationship. Furthermore this study is the first attempt at applying the approach to advertisement recommendation. The authors introduce an empirical measure for obtaining the weight values of dimensions, and present the similarity measure model, which enhances accuracy and convergence in advertisement recommendations. The experiment and evaluation show that our approach outperforms the previously reported fuzzy clustering technique.

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