GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Design and evaluation of a wide-area event notification service
ACM Transactions on Computer Systems (TOCS)
The many faces of publish/subscribe
ACM Computing Surveys (CSUR)
On Introducing Location Awareness in Publish-Subscribe Middleware
ICDCSW '05 Proceedings of the Fourth International Workshop on Distributed Event-Based Systems (DEBS) (ICDCSW'05) - Volume 04
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Design and Implementation of the Pervaho Middleware for Mobile Context-Aware Applications
MCETECH '08 Proceedings of the 2008 International MCETECH Conference on e-Technologies
Composite subscriptions in content-based publish/subscribe systems
Proceedings of the ACM/IFIP/USENIX 2005 International Conference on Middleware
Supporting mobility in content-based publish/subscribe middleware
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
A new model for context-aware transactions in mobile services
Personal and Ubiquitous Computing
Mining user similarity based on routine activities
Information Sciences: an International Journal
A context-aware cross-layer broadcast model for ad hoc networks
Personal and Ubiquitous Computing
| Hi-index | 0.00 |
In pervasive environments, the Pub/Sub paradigm is regarded as an important means of information sharing and event dissemination. In this paper, we first analyze different context in Pub/Sub systems that has remarkable impacts upon user's satisfaction to event dissemination and then give corresponding strategies by exploiting time context and event-preference context so as to provide personalized event dissemination. That is, by leveraging time context, we provide the extended matching against long-standing events, and by leveraging event-preference context, we present the recommendation algorithm which is based on hidden Markov process. Performance analysis and experiment evaluation show that both strategies can improve user's experiences of event dissemination.