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Predictive Approach for User Long-Term Needs in Content-Based Image Suggestion

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2 Author(s)
Boutemedjet, S. ; Dept. d'Inf., Univ. de Sherbrooke, Sherbrooke, QC, Canada ; Ziou, D.

In this paper, we formalize content-based image suggestion (CBIS) as a Bayesian prediction problem. In CBIS, users provide the rating of images according to both their long-term needs and the contextual situation, such as time and place, to which they belong. Therefore, a CBIS model is defined to fit the distribution of the data in order to predict relevant images for a given user. Generally, CBIS becomes challenging when only a small amount of data is available such as in the case of “new users” and “new images.” The Bayesian predictive approach is an effective solution to such a problem. In addition, this approach offers efficient means to select highly rated and diversified suggestions in conformance with theories in consumer psychology. Experiments on a real data set show the merits of our approach in terms of image suggestion accuracy and efficiency.

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Neural Networks and Learning Systems, IEEE Transactions on  (Volume:23 ,  Issue: 8 )