User Modeling and User-Adapted Interaction
Talk amongst yourselves: inviting users to participate in online conversations
Proceedings of the 12th international conference on Intelligent user interfaces
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
That's what friends are for: facilitating 'who knows what' across group boundaries
Proceedings of the 2007 international ACM conference on Supporting group work
UM '07 Proceedings of the 11th international conference on User Modeling
Modelling Semantic Relationships and Centrality to Facilitate Community Knowledge Sharing
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Spreading the honey: a system for maintaining an online community
Proceedings of the ACM 2009 international conference on Supporting group work
Narcissus: Group and Individual Models to Support Small Group Work
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Motivating cooperation on peer to peer networks
UM'03 Proceedings of the 9th international conference on User modeling
Extending sound sample descriptions through the extraction of community knowledge
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Monitoring contributions online: a reputation system to model expertise in online communities
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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The effectiveness of personalized support provided to virtual communities depends on what we know about a particular community and in which areas the community may need support Following organizational psychology theories, we have developed algorithms to automatically detect patterns of knowledge sharing in a closely-knit virtual community, focusing on transactive memory, shared mental models, and cognitive centrality The automatic detection of problematic areas enables taking decisions about notifications targeted at different community members but aiming at improving the functioning of the community as a whole The paper presents graph-based algorithms for detecting community knowledge sharing patterns, and illustrates, based on a study with an existing community, how these patterns can be used for community-tailored support.