Abstract
This paper presents a Bayesian approach to address two important issues of image recommendation that are: (1) change in long-term needs of users and (2) evolution of image collections. Users are offered a new interaction modality which allows them to provide either positive or negative relevance feedback (RF) data to express their recent needs. Then, an efficient variational Online learning algorithm updates both user and product collection models by favoring recent RF data. The proposed method is general and can be applied in collaborative filtering. Experimental results demonstrate the importance of maintaining most up-to-date user models on the rating’s prediction accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Boutemedjet, S., Ziou, D.: A Graphical Model for Context-Aware Visual Content Recommendation. IEEE Trans. on Multimedia 10(1), 52–62 (2008)
Boutemedjet, S., Ziou, D., Bouguila, N.: A Graphical Model for Content Based Image Suggestion and Feature Selection. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 30–41. Springer, Heidelberg (2007)
Boutemedjet, S., Ziou, D., Bouguila, N.: Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data. In: Proc. of 21st Conf. on Advances in Neural Information Processing Systems (NIPS) (2007)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of 14th Conf. on Uncertainty in Artificial Intelligence (UAI), pp. 43–52 (1998)
Hofmann, T.: Latent Semantic Models for Collaborative Filtering. ACM Trans. on Information Systems 22(1), 89–115 (2004)
Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.: Simultaneous Feature Selection and Clustering Using Mixture Models. IEEE Trans. on Patt. Anal. and Mach. Intell. 26(9), 1154–1166 (2004)
Marlin, B.: Modeling User Rating Profiles For Collaborative Filtering. In: Proc. of 17th Conf. on Advances in Neural Information Processing Systems (NIPS) (2003)
McAlister, L., Pessemier, E.: Variety Seeking Behavior: An Interdisciplinary Review. The Journal of Consumer Research 9(3), 311–322 (1982)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: Proc. of the 18th Nat. Conf. on Artificial Intelligence (AAAI), pp. 187–192 (2002)
Minka, T.: Estimating a Dirichlet Distribution (2003)
Sato, M.A.: Online Model Selection Based on the Variational Bayes. Neural Computation 13(7), 1649–1681 (2001)
Si, L., Jin, R.: Flexible Mixture Model for Collaborative Filtering. In: Proc. of 20th Int. Conf. on Machine Learning (ICML), pp. 704–711 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boutemedjet, S., Ziou, D. (2009). Variational Bayesian Approach for Long-Term Relevance Feedback. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-01307-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01306-5
Online ISBN: 978-3-642-01307-2
eBook Packages: Computer ScienceComputer Science (R0)