A Collaborative Filtering Approach to Personalized Interactive Entertainment using MPEG-21

In this chapter we present an integrated framework for personalized access to interactive entertainment content, using characteristics from the emerging MPEG-21 standard. Our research efforts focus on multimedia content presented within the framework set by today’s movie content broadcasting over a variety of networks and terminals, i.e. analogue and digital television broadcasts, video on mobile devices, personal digital assistants and more. This work contributes to the bridging of the gap between the content and the user, providing end-users with a wide range of real-time interactive services, ranging from plain personalized statistics and optional enhanced in-play visual enhancements to a fully userand content-adaptive platform. The proposed approach implements and extends in a novel way a well-known collaborative filtering approach; it applies a hierarchical clustering algorithm on the data towards the scope of group modelling implementation. It illustrates also the benefits from the MPEG-21 components utilization in the process and analyzes the importance of the Digital Item concept, containing both the (binary) multimedia content, as well as a structured representation of the different entities that handle the item, together with the set of possible actions on the item. Finally, a use case scenario is presented to illustrate the entire procedure. The core of this work is the novel group modelling approach, on top of the hybrid collaborative filtering algorithm, employing principles of taxonomic knowledge representation and hierarchical clustering theory. The outcome of this framework design is the fact that end-users are presented with personalized forms of multimedia content, thus enhancing their viewing experience and creating more revenue opportunities to content providers.