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.
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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
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DOI: https://doi.org/10.1007/978-3-642-01307-2_31
Publisher Name: Springer, Berlin, Heidelberg
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