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Automatic Recommendations for Machine-Assisted Multimedia Annotation: A Knowledge-Mining Approach

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Semantic Multimedia (SAMT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4816))

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Abstract

Recommender systems apply knowledge discovery techniques to help in finding associated information. In this paper, we investigate the use of association rule mining as an underlying technology for a recommender system aimed at improving the annotation process of multimedia news documents. The accuracy of these systems is very sensitive to the number of already annotated news items (the ”cold-start”- problem); ontology-based semantic relations are being used to alleviate this situation.

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References

  1. MESH: Multimedia semantic syndication for enhanced news services, http://www.mesh-ip.eu

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Authors and Affiliations

  1. Telefónica I+D, Valladolid, Spain

    Mónica Díez & Paulo Villegas

Authors
  1. Mónica Díez
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  2. Paulo Villegas
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Editor information

Bianca Falcidieno Michela Spagnuolo Yannis Avrithis Ioannis Kompatsiaris Paul Buitelaar

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© 2007 Springer-Verlag Berlin Heidelberg

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Díez, M., Villegas, P. (2007). Automatic Recommendations for Machine-Assisted Multimedia Annotation: A Knowledge-Mining Approach. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds) Semantic Multimedia. SAMT 2007. Lecture Notes in Computer Science, vol 4816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77051-0_10

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  • DOI: https://doi.org/10.1007/978-3-540-77051-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77033-6

  • Online ISBN: 978-3-540-77051-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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