Rough Set Theory Based User Aware TV Program and Settings Recommender

Rough Set Theory Based User Aware TV Program and Settings Recommender

Thyagaraju G.S. (Sri Dharmasthala Manjunatheshwara College of Engineering and Technology Research Centre, Visveswaraiah Technological University, India) and U.P. Kulkarni (Sri Dharmasthala Manjunatheshwara College of Engineering and Technology Research Centre, Visveswaraiah Technological University, India)
DOI: 10.4018/japuc.2012040105
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Abstract

In this paper the authors are proposing a design of TV program and settings recommendation engine utilizing contextual parameters like personal, social, temporal, mood, and activity. In addition to the contextual parameters the system utilizes the explicit or implicit user ratings and watching history to resolve the conflict if any while recommending the services. The System is implemented exploiting AI techniques like fuzzy logic and Rough Sets Based Decision Rules. The motivation behind the proposed work is i) to improve the user’s satisfaction level and ii) to improve the social relationship between user and TV. The context aware recommender utilizes social context data as an additional input to the recommendation task alongside information of users and TV programs. They have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system for small families.
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Socialization and Personalization of consumer devices is an active research topic. A general definition of socialization is to make someone behave in a way that is acceptable to society. Personalization is “understanding the needs of each individual and helping satisfy a goal that efficiently and knowledgeably addresses each individual’s need in a given context.” Personalization and Socialization has really gained importance with always connected services in the context aware applications. Context aware applications and services use context information to provide relevant services to the user and task at hand (Shin & Woo, 2009; Williams, Ursu, & Kegel, 2009; Chandler & Ruckwood, 2005; Riecken, 2000; Kabir, Han, & Colman, 2011; Datia, Moura-Pires, Cardoso, & Pita, 2005; Jeffrey, 2007).

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