Skip to Main Content
Due to the rapid increase of contents available under the convergence of broadcasting and Internet, efficient access to personally preferred contents has become an important issue. In this paper, an automatic recommendation scheme based on collaborative filtering is presented for intelligent personalization of (IP)TV services. The proposed scheme does not require TV viewers (users) to make explicit ratings on their watched TV program contents. Instead, it implicitly infers the users' interests on the watched TV program contents. For the recommendation of user preferred TV program contents, our proposed recommendation scheme first clusters TV users into similar groups based on their preferences on the content genres from the user's watching history of TV program contents. For the personalized recommendation of TV program contents to an active user, a candidate set of preferred TV program contents is obtained via collaborative filtering for the group to which the active user belongs. The candidate TV programs for recommendation are then ranked by a proposed novel ranking model. Finally, a set of top- N ranked TV program contents is recommended to the active user. The experimental results show that the proposed TV program recommendation scheme yields about 77% of average precision accuracy and 0.135 value of ANMRR (Average Normalized Modified Retrieval Rank) with top five recommendations for 1,509 people.