Personal TVware: An Infrastructure to Support the Context-Aware Recommendation for Personalized Digital TV

 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 PersonalTVware - 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.