Related Experiment Videos
Interaction mining and skill-dependent recommendations for multi-objective team composition.
Christoph Dorn1, Florian Skopik, Daniel Schall
1Institute for Software Research, University of California, Irvine, CA 92697-3455, USA.
Data & Knowledge Engineering
|February 3, 2012
Summary
This study introduces a novel approach to team formation, balancing skills and connectivity using genetic algorithms and simulated annealing. It enhances collaboration networks by incorporating implicit recommendations, even in sparse environments.
Area of Science:
- Computer Science
- Social Network Analysis
- Human-Computer Interaction
Background:
- Web 2.0 technologies facilitate monitoring and analysis of human interactions and team dynamics.
- Effective team composition necessitates balancing skill coverage with social connectivity.
- Understanding interaction structures is crucial for predicting collaboration success.
Purpose of the Study:
- To address an extended team formation problem incorporating both direct interactions and implicit recommendations.
- To develop heuristics for discovering efficient team configurations balancing skill coverage and connectivity.
- To create a self-adjusting mechanism for optimizing direct interactions and recommendations in network connectivity.
Main Methods:
- Utilized Genetic Algorithms and Simulated Annealing for heuristic-based team configuration discovery.
- Developed a self-adjusting mechanism to integrate direct interactions and implicit recommendations for network connectivity.
- Evaluated the approach using simulated collaboration networks mirroring real-world expert networks.
Main Results:
- Demonstrated the successful identification of efficient team configurations.
- Showcased the algorithm's robustness in maintaining performance with up to 40% expert removal.
- Validated the approach's effectiveness across diverse social network configurations.
Conclusions:
- The proposed heuristics effectively balance skill coverage and team connectivity in complex networks.
- Implicit recommendations significantly enhance collaboration in sparsely connected networks.
- The method provides a robust solution for optimizing team formation in dynamic online environments.
Related Concept Videos
01:06Collisions in Multiple Dimensions: Problem Solving
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
00:48Optimal Foraging
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
01:14Decision Making: Traditional Method
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
04:49Robbers Cave
During the 1950s, the landmark Robbers Cave experiment demonstrated that when groups must compete with one another, intergroup conflict, hostility, and even violence may result. At the Oklahoman summer camp, two troops of boys—termed the Rattlers and the Eagles—took part in a week-long tournament. During this time, their negativity culminated in derogatory name-calling, fistfights, and even vandalism and destruction of property. However, this work also revealed that such tension could be...
01:37Social Loafing
Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated, individuals become less...