Assessing Conceptual Similarity to Support Concept Mapping
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Knowledge based approach to semantic composition of teams in an organization
Proceedings of the 2005 ACM symposium on Applied computing
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Cognitive agents based simulation for decisions regarding human team composition
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Agent trust evaluation and team formation in heterogeneous organizations
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Conversational Case-Based Recommendations Exploiting a Structured Case Model
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
IEEE Transactions on Knowledge and Data Engineering
Team recommendation in open innovation networks
Proceedings of the third ACM conference on Recommender systems
The adaptive web
Learning About the Quality of Teamwork from Wikiteams
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Location-based team recommendation in computer gaming scenarios
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
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Team recommendation is required for composing an appropriate team for a particular task or project by selecting/choosing among the adequate/best team members. Usually project managers decide how to compose a team based on their experience in similar projects. Given this best practice we propose to algorithmically compose appropriate teams for a task by applying case-based reasoning on a previously developed meta-model for team recommendation. We evaluate our approach through comparing the ranking given by a domain expert with the result of our recommender and conclude with a discussion of these results.