Fab: content-based, collaborative recommendation
Communications of the ACM
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Managing User Preferences for Personalization in a Pervasive Service Environment
AICT '07 Proceedings of the The Third Advanced International Conference on Telecommunications
User-centric services provisioning in wireless environments
Communications of the ACM - Remembering Jim Gray
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
A context management framework for supporting context-aware distributed applications
IEEE Communications Magazine
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Personalization has a key role to play in making a ubiquitous computing system more usable to the end user. Our goal is to provide the end user with two-step personalization services. To do this, we propose the personalized situation model consisted of user behavior and user preferences models. The former is to set up a weighted list of usefol services according to user behavior patterns of current situation. The latter is to set up a weighted list of specific contents according to the service selected by a user. That is to say, two-step personalization services are consisted of service recommendation step and contents recommendation step. As a result, if user selected TV service among service categories to be recommended at the first step, it should match a user's desired TV programs and recommend TV programs with high user preference at the second step.