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International Conference on Multimedia Modeling

MMM 2016: MultiMedia Modeling pp 361–366Cite as

Applying Visual User Interest Profiles for Recommendation and Personalisation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9517))

Abstract

We propose that a visual user interest profile can be generated from images associated with an individual. By employing deep learning, we extract a prototype visual user interest profile and use this as a source for subsequent recommendation and personalisation. We demonstrate this technique via a hotel booking system demonstrator, though we note that there are numerous potential applications.

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Acknowledgments

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland

    Jiang Zhou, Rami Albatal & Cathal Gurrin

Authors
  1. Jiang Zhou
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  2. Rami Albatal
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  3. Cathal Gurrin
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Corresponding author

Correspondence to Cathal Gurrin .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Zhou, J., Albatal, R., Gurrin, C. (2016). Applying Visual User Interest Profiles for Recommendation and Personalisation. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_34

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  • DOI: https://doi.org/10.1007/978-3-319-27674-8_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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