Masaaki Hoshino

Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system

TV Program recommendation is a good example of a novel application of networked appliances using personalization technologies. The aim of this paper is to propose methods to improve the accuracy of TV program recommendation. Automatic metadata expansion (AME) is a method to enhance TV program metadata from electronic program guide (EPG) data, and indirect collaborative filtering (ICF) is a method to recommend non-persistent items such as TV programs based on the preferences of other members in a community. In this paper, the effectiveness of these methods is confirmed through experiments. This online TV recommendation system is currently being used by 230,000 members in Japan. The result of the actual operation is also discussed.

Cross-category Recommendation for Multimedia Content

The purpose of this article is to introduce cross-category recommendation technologies for multimedia content. First, in order to understand how to realize the recommendation function, multimedia content recommendation technologies and cross-category recommendation technologies are outlined. Second, practical applications and services using these technologies are described. Finally, difficulties involving cross-category recommendation for multimedia content and future prospects are mentioned as the conclusion.