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

Topalli I., KILINÇ S.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.28, sa.3, ss.1567-1583, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 28 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3906/elk-1909-139
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1567-1583
  • Anahtar Kelimeler: Artificial neural networks, genetic algorithms, hybrid broadcast broadband television, program recommendation, user profiling, EVOLUTION
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.