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Scientific Reports:

C. Dorn, F. Skopik, D. Schall, S. Dustdar:
"Interaction Mining and Skill-dependent Recommendations for Multi-objective Team Composition";
Report No. TUV-1841-2011-03, 2011; 29 pages.



English abstract:
Web-based collaboration and virtual environments supported by various
Web 2.0 concepts enable the application of numerous monitoring, mining
and analysis tools to study human interactions and team formation processes.
The composition of an effective team requires a balance between
adequate skill fulfillment and sufficient team connectivity. The underlying
interaction structure reflects social behavior and relations of individuals
and determines to a large degree how well people can be expected to collaborate.
In this paper we address an extended team formation problem
that does not only require direct interactions to determine team connectivity
but additionally uses implicit recommendations of collaboration partners
to support even sparsely connected networks. We provide two heuristics
based on Genetic Algorithms and Simulated Annealing for discovering efficient
team configurations that yield the best trade-off between skill coverage
and team connectivity. Our self-adjusting mechanism aims to discover
the best combination of direct interactions and recommendations when deriving
connectivity. We evaluate our approach based on multiple configurations
of a simulated collaboration network that features close resemblance
to real world expert networks. We demonstrate that our algorithm successfully
identifies efficient team configurations even when removing up to 40%
of experts from various social network configurations.

Keywords:
team formation, social network, composition heuristic, recommendation trade-off model


Related Projects:
Project Head Schahram Dustdar:
COIN

Project Head Schahram Dustdar:
Commius


Created from the Publication Database of the Vienna University of Technology.