[Back]

C. Dorn, F. Skopik, D. Schall, S. Dustdar:
"Interaction mining and skill-dependent recommendations for multi-objective team composition";
Data & Knowledge Engineering, Volume 70 (2011), Issue 10; 866 - 891.

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 selfadjusting
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.

Team formation Social network Composition heuristic Recommendation trade-off model

http://dx.doi.org/10.1016/j.datak.2011.06.004

Project Head Schahram Dustdar:
COIN

Project Head Schahram Dustdar:
Commius