Gain-based selection of ambient media services in pervasive environments
Mobile Networks and Applications
Variational Bayesian Approach for Long-Term Relevance Feedback
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A discrete mixture-based kernel for SVMs: Application to spam and image categorization
Information Processing and Management: an International Journal
Personalized movie recommendation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Model-based subspace clustering of non-Gaussian data
Neurocomputing
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Multimedia Tools and Applications
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Context-aware social media recommendation based on potential group
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
Image context discovery from socially curated contents
Proceedings of the 21st ACM international conference on Multimedia
Intelligent context retrieval and management for services in the Internet of Things
International Journal of Ad Hoc and Ubiquitous Computing
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Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.