Query enhancement by user profiles
Proc. of the third joint BCS and ACM symposium on Research and development in information retrieval
Web Site Personalization Using User Profile Information
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Mobile and ad hoc local networks
Ontology Based Personalized Search
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
A Rule-Based Approach Towards Context-Aware User Notification Services
PERSER '06 Proceedings of the 2006 ACS/IEEE International Conference on Pervasive Services
Ontology-based user preference modeling for enhancing interoperability in personalized services
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: applications and services
AIRSF: a new entertainment adaptive framework for stress free air travels
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
User profile management: reference model and web services implementation
International Journal of Web and Grid Services
Unobtrusive physiological monitoring in an airplane seat
Personal and Ubiquitous Computing
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This paper first presents an adaptive user preference model for personalized service delivery systems. In this model, user preference is modeled by a two-layer tree with dynamic changeable structures. The top layer of the tree is used for modeling user's long term service preference. Each node represents user's long term evolving commitment to certain categories of service. The lower layer of the tree is used for modeling user spontaneous service requirement which depends on context of use. Each node relates one context of use to one or more desired service requirements. The tree is dynamically constructed by the formal relation definitions among nodes. The advantage of this structure is three folds: (1) it can not only model the user's long term but also spontaneous preference items. More over, the relations between all preference items are formally defined; (2) if the number of preference items is many, it is more efficient and easier to find the right preference items; (3) if the user desired service has been removed, the system can utilize the personalized hierarchy service structure of the preference tree to calculate and recommend similar services. After the introduction of the user preference modeling, an algorithm of how to build it is presented. Finally, we customized the user preference for personalized in-flight entertainment recommendation to validate its features.