DocumentCode :
1541144
Title :
Predictive Approach for User Long-Term Needs in Content-Based Image Suggestion
Author :
Boutemedjet, S. ; Ziou, Djemel
Author_Institution :
Dept. d´Inf., Univ. de Sherbrooke, Sherbrooke, QC, Canada
Volume :
23
Issue :
8
fYear :
2012
Firstpage :
1242
Lastpage :
1253
Abstract :
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.
Keywords :
Bayes methods; content-based retrieval; image retrieval; Bayesian prediction problem; Bayesian predictive approach; CBIS; consumer psychology; content-based image suggestion; Accuracy; Bayesian methods; Content based retrieval; Context; Data models; Predictive models; Bayesian learning; collaborative filtering (CF); feature selection; mixture models;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2012.2199765
Filename :
6218198
Link To Document :
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