Abstract
In the modern age, cross-media production is an innovative technique used by the media industry to ensure a positive return on investments while optimising productivity and market coverage. So that, technologies for the seamless fusion of heterogeneous data streams are increasingly considered important, and thus research efforts have started to explore this area. After having aggregated heterogeneous sources, what has to be addressed as the next problem is efficiency in retrieving and using the produced content. Tools for personalised and context-oriented multimedia retrieval are indispensable to access desired content from the aggregated data in a quicker and more useful way. This chapter describes the problems connected with this scenario and proposes an innovative technological framework to solve them, in the area of informative content (news) distribution and retrieval. Extensive experiments prove the effectiveness of this approach in a real-world business context.
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Messina, A., Montagnuolo, M. (2010). Multimodal Aggregation and Recommendation Technologies Applied to Informative Content Distribution and Retrieval. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds) Information Retrieval and Mining in Distributed Environments. Studies in Computational Intelligence, vol 324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16089-9_12
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DOI: https://doi.org/10.1007/978-3-642-16089-9_12
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