ambientROOM: integrating ambient media with architectural space
CHI 98 Cconference Summary on Human Factors in Computing Systems
Communications of the ACM
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Semantic-based framework for personalised ambient media
Multimedia Tools and Applications
Context-aware content filtering & presentation for pervasive & mobile information systems
Proceedings of the 1st international conference on Ambient media and systems
Ambient Intelligence: A Multimedia Perspective
IEEE MultiMedia
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
User preference learning for multimedia personalization in pervasive computing environment
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Contextually aware information delivery in pervasive computing environments
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
A framework for human-centered provisioning of ambient media services
Multimedia Tools and Applications
Semantic ambient media--an introduction
Multimedia Tools and Applications
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This paper presents a mechanism for dynamically estimating the gain in ambient media services. Gain estimation is an attempt to measure the extent to which a certain media service is useful for a user in a particular context. Such a measure may provide invaluable support for personalizing a user's ambient environment through the selection of timely, context-aware and interesting media services. The proposed method considers multiple factors including the user's profile, context, media reputation, and the interaction history to dynamically estimate the gain of a service. The gain estimation method has been incorporated in a prototype smart mirror system in a smart environment setting. Experimental results demonstrate the suitability of the proposed method.