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Visual Aspect: A Unified Content-Based Collaborative Filtering Model for Visual Document Recommendation

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Image Analysis and Recognition (ICIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4141))

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Abstract

This paper presents a generative graphical model (VC-Aspect) for filtering visual documents such as images. The proposed VC-Aspect extends the well-known Aspect model and combines both content based and collaborative filtering approaches in a unified framework. Instead of considering item indices in the model such as model-based collaborative filtering techniques, we use visual features in describing visual documents. This allows the model to predict ratings for new visual documents with the same set of parameters. Experimental results show the usefulness of such an approach in a real life application such as the content based image retrieval.

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Author information

Authors and Affiliations

  1. DI, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada

    Sabri Boutemedjet & Djemel Ziou

Authors
  1. Sabri Boutemedjet
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  2. Djemel Ziou
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Editor information

Editors and Affiliations

  1. Departamento de Engenharia Electrotécnica e de Computadores da, Faculdade de Engenharia da, Universidade do Porto, Campus FEUP, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed S. Kamel

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© 2006 Springer-Verlag Berlin Heidelberg

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Boutemedjet, S., Ziou, D. (2006). Visual Aspect: A Unified Content-Based Collaborative Filtering Model for Visual Document Recommendation. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_63

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  • DOI: https://doi.org/10.1007/11867586_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44891-4

  • Online ISBN: 978-3-540-44893-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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