A TV News Recommendation System with Automatic Recomposition
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
ANTS: A Complete System for Automatic News Programme Annotation Based on Multimodal Analysis
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
An adaptive personalized news dissemination system
Journal of Intelligent Information Systems
Proceedings of the 18th international conference on World wide web
ICWE '9 Proceedings of the 9th International Conference on Web Engineering
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Ontology-based news recommendation
Proceedings of the 2010 EDBT/ICDT Workshops
3rd international workshop on automated information extraction in media production
Proceedings of the international conference on Multimedia
The hyper media news system for multimodal and personalised fruition of informative content
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Discrimination of media moments and media intervals: sticker-based watch-and-comment annotation
Multimedia Tools and Applications
News recommendation via hypergraph learning: encapsulation of user behavior and news content
Proceedings of the sixth ACM international conference on Web search and data mining
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This paper presents an implicit news recommender system based on the user's interests and multimodal content analysis. Multimodality is here intended as the capability of integrating multiple modes, e.g. radio-television channels and Web sites, and different media, e.g. written text and spoken content. Personal interests are inferred by natural language processing of the users' blogs. Latent semantic analysis is used to find the relationships between such interests and both online newspaper articles and broadcast news stories. The novelty of this system is the ability to treat equally and simultaneously online press reports and TV news streams. Experiments in a long-term real-world usage scenario demonstrate the quality of the proposed recommendations.