An MHP framework to provide intelligent personalized recommendations about digital TV contents

Digital Television will bring a significant increase in the amount of channels and programs available to end users, with many more difficulties to find contents appealing to them among a myriad of irrelevant information. Thus, automatic content recommenders should receive special attention in the following years to improve their assistance to users. The current content recommenders have important deficiencies that hamper their wide acceptance. In this paper, we present a new approach for automatic content recommendation that significantly reduces those deficiencies. This approach, based on Semantic Web technologies, has been implemented in the AdVAnced Telematic search of Audiovisual contents by semantic Reasoning tool, a hybrid content recommender that makes extensive use of well-known standards, such as Multimedia Home Platform, TV-Anytime and OWL. Also, we have carried out an experimental evaluation, the results of which show that our proposal performs better than other existing approaches. Copyright © 2007 John Wiley & Sons, Ltd.

[1]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[2]  Jerry R. Hobbs,et al.  An ontology of time for the semantic web , 2004, TALIP.

[3]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[4]  John Zimmerman,et al.  TV Personalization System , 2004, Personalized Digital Television.

[5]  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.

[6]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[7]  R.M. Rast,et al.  The dawn of digital TV , 2005, IEEE Spectrum.

[8]  Rebeca P. Díaz-Redondo,et al.  ATLAS: a framework to provide multiuser and distributed t-learning services over MHP , 2006 .

[9]  Fabio Bellifemine,et al.  User Modeling and Recommendation Techniques for Personalized Electronic Program Guides , 2004, Personalized Digital Television.

[10]  Philip M. Napoli.  Audience Economics: Media Institutions and the Audience Marketplace , 2003 .

[11]  Kamal Ali,et al.  TiVo: making show recommendations using a distributed collaborative filtering architecture , 2004, KDD.

[12]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[13]  George Sciadas,et al.  Unveiling the Digital Divide , 2002 .

[14]  Stuart Edward Middleton,et al.  Capturing knowledge of user preferences with recommender systems , 2003 .

[15]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[16]  Frank van Harmelen,et al.  A semantic web primer , 2004 .

[17]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[18]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

[19]  Jorge García Duque,et al.  Provision of distance learning services over Interactive Digital TV with MHP , 2008, Comput. Educ..

[20]  Patrick Baudisch,et al.  TV Scout: Guiding Users from Printed TV Program Guides to Personalized TV Recommendation , 2002 .

[21]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[22]  Lars Schmidt-Thieme,et al.  Exploiting Semantic Product Descriptions for Recommender Systems , 2004 .

[23]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[24]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[25]  Alan F. Smeaton,et al.  Improving the Quality of the Personalized Electronic Program Guide , 2004, User Modeling and User-Adapted Interaction.

[26]  Jennifer Widom,et al.  Exploiting hierarchical domain structure to compute similarity , 2003, TOIS.