User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks

In this paper, an earlier method proposed by the authors to make smart recommendations utilizing artificial intelligence and the latest technologies developed for the television area is expanded further using controlled clustering with genetic algorithms CCGA . For this purpose, genetic algorithms GAs , artificial neural networks ANNs , and hybrid broadcast broadband television HbbTV are combined to get the users' television viewing habits and to create profiles. Then television programs are recommended to the users based on that profiling. The data gathered by the developed HbbTV application for previous studies are reused in this study. These data are employed to cluster users. The number of clusters is found by CCGA, a method proposed in this paper. For each cluster formed by CCGA, a separate ANN is designed to learn the viewing habits of the users of the corresponding cluster. The weight matrices are initialized also by GA. The recommendations produced using the proposed model are then presented by the same HbbTV application developed by the authors. Clustering with GAs gives better results when compared to the well-known K-means clustering algorithm.

Keywords:

Artificial neural networks, genetic algorithms, hybrid broadcast broadband television program recommendation, user profiling,

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Turkish Journal of Electrical Engineering and Computer Science-Cover

ISSN: 1300-0632
Yayın Aralığı: Yılda 6 Sayı
Yayıncı: TÜBİTAK

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