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Finding a Team of Skilled Players Based on Harmony
We study the problem of finding a team of candidates with satisfactory level of chemistry from a graph. Given a pool of candidates, the problem requires finding a team of experts to perform a given task. Surprisingly, although ongoing research evaluated an abundance of variations with different cons...
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| creator | Afshar, Jafar Haghighian Roudsari, Arousha Lee, Wookey |
| description | We study the problem of finding a team of candidates with satisfactory level of chemistry from a graph. Given a pool of candidates, the problem requires finding a team of experts to perform a given task. Surprisingly, although ongoing research evaluated an abundance of variations with different constraints, it has overlooked a critical factor that has a huge impact in team formation task: team chemistry. In this paper, we address this challenging gap by proposing a bi-objective chemistry function. Our function embeds both mastery levels and interactions of the team members who not only collaborate with each other in an effective manner, but also possess an excellent level of mastery in their assigned task. We prove that this problem is NP-hard and then propose an algorithm to generate a team so that the chemistry among the members is maximized. Extensive experiments conducted on real dataset demonstrate the effectiveness of our method in addressing the problem. |
| doi_str_mv | 10.1109/BigComp48618.2020.00-33 |
| format | conference_proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2375-9356 |
| ispartof | 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), 2020, p.435-437 |
| issn | 2375-9356 |
| language | eng |
| recordid | cdi_ieee_primary_9070751 |
| source | IEEE Xplore All Conference Series |
| subjects | Aggregates Chemistry Collaboration Data mining interaction mastery Minimization Social network services Task analysis team-formation |
| title | Finding a Team of Skilled Players Based on Harmony |
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