A web-based IPTV content syndication system for personalized content guide

In this paper, we propose a web-based content syndication system in which users can easily choose Internet protocol television (IPTV) contents. This system generates personalized content guide to provide a list of IPTV contents with respect to users' interests and statistics information of their online social community. For this, IPTV contents and relevant metadata are collected from various sources and transformed. Then, the service and content metadata are processed by user metadata including audience measurement and community metadata. The metadata flows are separated from content flows of transport network. The implementation of IPTV content syndication system demonstrates how to arrange IPTV contents efficiently from content providers to the end user's screen. We also show that the user metadata including online community information are important for the system's performance and the user's satisfaction.

[1]  Shinjee Pyo,et al.  An Automatic Recommendation Scheme of TV Program Contents for (IP)TV Personalization , 2011, IEEE Transactions on Broadcasting.

[2]  Han-Gyu Ko,et al.  Generation of Semantic Clouds Based on Linked Data for Efficient Multimedia Semantic Annotation , 2011, ICWE Workshops.

[3]  Gyu Myoung Lee,et al.  Web-Based Personalized IPTV Services over NGN , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

[4]  David Geerts,et al.  Supporting the social uses of television: sociability heuristics for social tv , 2009, CHI.

[5]  Gyu Myoung Lee,et al.  Functional Architecture for NGN-Based Personalized IPTV Services , 2009, IEEE Transactions on Broadcasting.

[6]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[7]  Han-Gyu Ko,et al.  Semantically-based recommendation by using semantic clusters of users' viewing history , 2014, 2014 International Conference on Big Data and Smart Computing (BIGCOMP).

[8]  Frank Bentley,et al.  Ambient social tv: drawing people into a shared experience , 2008, CHI.

[9]  Xingshe Zhou,et al.  TV3P: an adaptive assistant for personalized TV , 2004, IEEE Transactions on Consumer Electronics.

[10]  Han-Gyu Ko,et al.  A community recommendation method based on social networks for web 2.0-based IPTV , 2009, 2009 16th International Conference on Digital Signal Processing.

[11]  Joachim Köhler,et al.  LIVE: An Integrated Production and Feedback System for Intelligent and Interactive TV Broadcasting , 2011, IEEE Transactions on Broadcasting.

[12]  José Juan Pazos-Arias,et al.  AVATAR: an improved solution for personalized TV based on semantic inference , 2006, IEEE Transactions on Consumer Electronics.

[13]  Rittwik Jana,et al.  IMS-TV: An IMS-based architecture for interactive, personalized IPTV , 2008, IEEE Communications Magazine.

[14]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[15]  Bernard Mérialdo,et al.  Automatic construction of personalized TV news programs , 1999, MULTIMEDIA '99.

[16]  Han-Gyu Ko,et al.  A Blog-Centered IPTV Environment for Enhancing Contents Provision, Consumption, and Evolution , 2010, ICWE.

[17]  Sherali Zeadally,et al.  Internet Protocol Television (IPTV): Architecture, Trends, and Challenges , 2011, IEEE Systems Journal.