Amortized efficiency of list update and paging rules
Communications of the ACM
The competitiveness of on-line assignments
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
The mythical man-month (anniversary ed.)
The mythical man-month (anniversary ed.)
On-line routing of virtual circuits with applications to load balancing and machine scheduling
Journal of the ACM (JACM)
Online computation and competitive analysis
Online computation and competitive analysis
Approximation algorithms
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Bounds on multiprocessing anomalies and related packing algorithms
AFIPS '72 (Spring) Proceedings of the May 16-18, 1972, spring joint computer conference
Formal Models for Expert Finding on DBLP Bibliography Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Concentration of Measure for the Analysis of Randomized Algorithms
Concentration of Measure for the Analysis of Randomized Algorithms
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Determining expert profiles (with an application to expert finding)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Estimating entity importance via counting set covers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Capacitated team formation problem on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 21st ACM international conference on Information and knowledge management
Composing activity groups in social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient bi-objective team formation in social networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Efficient algorithms for team formation with a leader in social networks
The Journal of Supercomputing
A strategy of multi-criteria decision-making task ranking in social-networks
The Journal of Supercomputing
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The internet has enabled the collaboration of groups at a scale that was unseen before. A key problem for large collaboration groups is to be able to allocate tasks effectively. An effective task assignment method should consider both how fit teams are for each job as well as how fair the assignment is to team members, in terms that no one should be overloaded or unfairly singled out. The assignment has to be done automatically or semi-automatically given that it is difficult and time-consuming to keep track of the skills and the workload of each person. Obviously the method to do this assignment must also be computationally efficient. In this paper we present a general framework for task assignment problems. We provide a formal treatment on how to represent teams and tasks. We propose alternative functions for measuring the fitness of a team performing a task and we discuss desirable properties of those functions. Then we focus on one class of task-assignment problems, we characterize the complexity of the problem, and we provide algorithms with provable approximation guarantees, as well as lower bounds. We also present experimental results that show that our methods are useful in practice in several application scenarios.