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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kobsa, A.: Generic user modeling systems. User Modeling and User-Adapted Interaction 11(1-2), 49–63 (2001)
Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction 11(3), 203–259 (2001)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Mooney, R., Roy, L.: Content-based book recommending using learning for text categorization. In: Proc. Fifth ACM Conf. Digital Libaries, pp. 195–204 (2000)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceeding of the ACM 1994 Conference on Computer Supported Cooperative Work (1994)
Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, July 24-26, pp. 43–52. University of Wisconsin Business School, Madison (1998)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of Twenty-second Annual International SIGIR Conference (1999)
Si, L., Jin, R.: Flexible mixture model for collaborative filtering. In: Machine Learning, Proceedings of Twentieth International Conference (ICML 2003), pp. 704–711. AAAI Press, Menlo Park (2003)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Marlin, B.: Collaborative filtering: A machine learning perspective. Master’s thesis, University of Toronto (2004)
Kim, C.Y., Lee, J.K., Cho, Y.H., Kim, D.H.: Viscors: A visual-content recommender for the mobile web. IEEE Intelligent Systems 19(6), 32–39 (2004)
Bouguila, N., Ziou, D., Vaillancourt, J.: Unsupervised learning of a finite mixture model based on the dirichlet distribution and its applications. IEEE Transactions on Image Processing 13(11), 1533–1543 (2004)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B 39, 1–38 (1977)
Herlocker, J.L.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Kherfi, M., Ziou, D., Bernardi, A.: Combining positive and negative examples in relevance feedback for content-based image retrieval. Journal of Visual Communication and Image Representation 14(4), 428–457 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)