Abstract
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.
Highlights
Before the digitalization of TV broadcasts, there was a limited number of TV channels available to viewers and viewers had only a few programs shown at the same time to choose among
In our previous works [1,2,3], we developed an hybrid broadcast broadband television (HbbTV) application for collecting users’ program-watching history, which was stored and processed on the receiver side
A complete method, including data collection at the client side, intelligent learning at the server side and program recommendation again at the client side is proposed, implemented, and validated. This method is shown by developing an HbbTV application
Summary
Before the digitalization of TV broadcasts, there was a limited number of TV channels available to viewers and viewers had only a few programs shown at the same time to choose among. Once the application is deployed by broadcasters and the constructed model is running on the server side, all consenting users’ data will be collected on the server and these users will be able to receive recommendations via HbbTV, which is already running on their receivers. The collected data were employed in the models proposed by the authors [1,2,3] for user profiling and program recommendation In this paper, these models are improved further by including a method that we call controlled clustering with genetic algorithms (CCGA). The main contributions of the paper are incorporating a platform-free approach (HbbTV) for TV program recommendation and using the new CCGA method to cluster collected data, which provide users’ TV-watching habits. Neurons in adjacent layers are fully connected through unidirectional links called synaptic weights
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
