Abstract-This paper describes a recommender system for sport videos, transmitted over the Internet and/or broadcast, in the context of large-scale events, which has been tested for the Olympic Games. The recommender is based on audiovisual consumption and does not depend on the number of users, running only on the client side. This avoids the concurrence, computation and privacy problems of central server approaches in scenarios with a large number of users, such as the Olympic Games.The system has been designed to take advantage of the information available in the videos, which is used along with the implicit information of the user and the modeling of his/her audiovisual content consumption. The system is thus transparent to the user, who does not need to take any specific action.Another important characteristic is that the system can produce recommendations for both live and recorded events.Testing has showed advantages compared to previous systems, as will be shown in the results.Index Terms-Audiovisual consumption, hidden Markov model (HMM), recommender system, sport videos.
This paper describes a novel content-based image recommendation system based on new image low level descriptors derived from the well known MPEG-7 parameters. Furthermore, it also proposes the integration of this recommendation system into a content-aware network architecture to enhance and enrich the content delivery and improve user's experience.
Among the major reasons for the success of the Internet have been the simple networking architecture and the IP interoperation layer. However, the traffic model has recently changed. More and more applications (e.g. peerto-peer, content delivery networks) target on the content that they deliver rather than on the addresses of the servers who (originally) published/hosted that content. This trend has motivated a number of content-oriented networking studies. In this paper we summarize some the most important approaches.
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