Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Model Selection Based on the Variational Bayes
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Graphical Model for Content Based Image Suggestion and Feature Selection
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Graphical Model for Context-Aware Visual Content Recommendation
IEEE Transactions on Multimedia
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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.