The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
PROJECT TEAM SELECTION USING FUZZY OPTIMIZATION APPROACH
Cybernetics and Systems
A team formation model based on knowledge and collaboration
Expert Systems with Applications: An International Journal
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Forming effective worker teams with multi-functional skill requirements
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
Power in unity: forming teams in large-scale community systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Team Formation for Generalized Tasks in Expertise Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Composing near-optimal expert teams: a trade-off between skills and connectivity
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Discovering top-k teams of experts with/without a leader in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Online team formation in social networks
Proceedings of the 21st international conference on World Wide Web
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We tackle the problem of finding a team of experts from a social network to complete a project that requires a set of skills. The social network is modeled as a graph. A node in the graph represents an expert and has a weight representing the monetary cost for using the expert service. Two nodes in the graph can be connected and the weight on the edge represents the communication cost between the two corresponding experts. Given a project, our objective is to find a team of experts that covers all the required skills and also minimizes the communication cost as well as the personnel cost of the project. To minimize both of the objectives, we define a new combined cost function which is based on the linear combination of the objectives (i.e. communication and personnel costs). We show that the problem of minimizing the combined cost function is an NP-hard problem. Thus, one approximation algorithm is proposed to solve the problem. The proposed approximation algorithm is bounded and the approximation ratio of the algorithm is proved in the paper. Three heuristic algorithms based on different intuitions are also proposed for solving the problem. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms.