Abstract
This paper proposes a TV viewer age-group classification method for family members based on TV watching history. User profiling based on watching history is very complex and difficult to achieve. To overcome these difficulties, we propose a probabilistic approach that models TV watching history with a Gaussian mixture model (GMM) and implements a feature-selection method that identifies useful features for classifying the appropriate age-group class. Then, to improve the accuracy of age-group classification, a multi-layered Bayesian classifier is applied for demographic analysis. Extensive experiments showed that our multi-layered classifier with GMM is valid. The accuracy of classification was improved when certain features were singled out and demographic properties were applied.
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References
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Acknowledgments
This work was supported by the Electronics and Telecommunications Research Institute (ETRI) R&D Program of Korea Communications Commission (KCC), Korea [11921-03001, “Development of Beyond Smart TV Technology”].
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Yi, C., Jeong, S., Han, KS., Lee, H. (2013). Age-Group Classification for Family Members Using Multi-Layered Bayesian Classifier with Gaussian Mixture Model. In: Park, J., Ng, JY., Jeong, HY., Waluyo, B. (eds) Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 240. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6738-6_142
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DOI: https://doi.org/10.1007/978-94-007-6738-6_142
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