Abstract
In this paper, we study the problem of Team Member Replacement - given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity). Prior studies in teamwork have suggested that a good team member replacement should bring synergy to the team in terms of having both skill matching and structure matching. However, existing techniques either do not cover both aspects or consider the two aspects independently. In this work, we propose a novel problem formulation using the concept of graph kernels that takes into account the interaction of both skill and structure matching requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly.
| Original language | English (US) |
|---|---|
| Title of host publication | WWW 2015 - Proceedings of the 24th International Conference on World Wide Web |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 636-646 |
| Number of pages | 11 |
| ISBN (Print) | 9781450334693 |
| DOIs | |
| State | Published - May 18 2015 |
| Event | 24th International Conference on World Wide Web, WWW 2015 - Florence, Italy Duration: May 18 2015 → May 22 2015 |
Other
| Other | 24th International Conference on World Wide Web, WWW 2015 |
|---|---|
| Country | Italy |
| City | Florence |
| Period | 5/18/15 → 5/22/15 |
Keywords
- Graph kernel
- Scalability
- Team member replacement
ASJC Scopus subject areas
- Computer Networks and Communications
- Software
Cite this
Replacing the irreplaceable : Fast algorithms for team member recommendation. / Li, Liangyue; Tong, Hanghang; Cao, Nan; Ehrlich, Kate; Lin, Yu Ru; Buchler, Norbou.
WWW 2015 - Proceedings of the 24th International Conference on World Wide Web. Association for Computing Machinery, Inc, 2015. p. 636-646.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Replacing the irreplaceable
T2 - Fast algorithms for team member recommendation
AU - Li, Liangyue
AU - Tong, Hanghang
AU - Cao, Nan
AU - Ehrlich, Kate
AU - Lin, Yu Ru
AU - Buchler, Norbou
PY - 2015/5/18
Y1 - 2015/5/18
N2 - In this paper, we study the problem of Team Member Replacement - given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity). Prior studies in teamwork have suggested that a good team member replacement should bring synergy to the team in terms of having both skill matching and structure matching. However, existing techniques either do not cover both aspects or consider the two aspects independently. In this work, we propose a novel problem formulation using the concept of graph kernels that takes into account the interaction of both skill and structure matching requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly.
AB - In this paper, we study the problem of Team Member Replacement - given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity). Prior studies in teamwork have suggested that a good team member replacement should bring synergy to the team in terms of having both skill matching and structure matching. However, existing techniques either do not cover both aspects or consider the two aspects independently. In this work, we propose a novel problem formulation using the concept of graph kernels that takes into account the interaction of both skill and structure matching requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly.
KW - Graph kernel
KW - Scalability
KW - Team member replacement
UR - http://www.scopus.com/inward/record.url?scp=84968765125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84968765125&partnerID=8YFLogxK
U2 - 10.1145/2736277.2741132
DO - 10.1145/2736277.2741132
M3 - Conference contribution
AN - SCOPUS:84968765125
SN - 9781450334693
SP - 636
EP - 646
BT - WWW 2015 - Proceedings of the 24th International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
ER -