Recommendation in the Digital TV Domain: An Architecture based on textual description analysis

Abstract

Recommendation systems have been used in several application domains, most recently for TV (Digital TV, Smart TV, etc.). Several approaches can be used to recommend items, tags, etc., mainly based on user feedback. However, in the Digital TV domain, user feedback has to be done generally by using the remote control, which should be avoided to improve user experience, since assigning explicit feedback to items is restricted by the characteristics of this domain (difficulties when typing with the remote control, etc.). Moreover, in the Smart TV environment several types of items can be recommended (movies, musics, books, etc.). Thus, the recommendation should be generic enough to suit to different content. To solve the problem of acquiring explicit feedback and still generate personalized recommendations to be used by different Smart TV applications, this work proposes a recommendation architecture based on the extraction and classification of terms by analyzing the textual descriptions of TV programs present on electronic programming guides. In order to validate the proposed solution, a prototype using a real dataset has been developed, showing that using the recommended terms it is possible to generate final recommendations for different Smart TV applications.