Abstract—The coming of the Digital TV will bring a significant increase in the number TV programs offered by TV operators. Consequently, the user are facing it difficulty to find out the most interesting TV programs among the various options available. In this new scenario, the recommender systems stand out as a possible solution to the information overload problem. However, the current approaches to recommend content for Digital TV rarely considers the context during the recommendation process. Thus, this paper presents a software infrastructure – entitled Personal TV ware - to support context-aware recommendation of TV programs. To validate the PersonalTVware, a context-aware recommender system was implemented as a concept proof. In order to evaluate the quality of the recommendation, a number of experiments have been conducted. The results indicate that consider both user’s profile and context can provide better recommendations.
Index Terms—Context-awareness, interactive digital TV, recommender systems, ubiquitous computing.
Authors are with the Laboratory of Computer Architecture and Network, University of São Paulo (USP), São Paulo, Brazil (e-mail: fsilva@larc.usp.br; luizgpa@larc.usp.br; gbressan@larc.usp.br).
Cite: Fábio Santos da Silva, Luiz Gustavo Pacola Alves, and Graça Bressan, "Personal TVware: An Infrastructure to Support the Context-Aware Recommendation for Personalized Digital TV," International Journal of Computer Theory and Engineering vol. 4, no. 2, pp. 131-136, 2012.
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