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Replacing the irreplaceable: Fast algorithms for team member recommendation. arXiv:1409.5512 (2014)
| Content Provider | CiteSeerX |
|---|---|
| Author | Li, Liangyue Tong, Hanghang Cao, Nan Ehrlich, Kate Lin, Yu-Ru Buchler, Norbou |
| Abstract | In this paper, we study the problem of Team Member Re-placement – 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 sug-gested that a good team member replacement should bring synergy to the team in terms of having both skill match-ing 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 for-mulation using the concept of graph kernels that takes into account the interaction of both skill and structure match-ing requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing ef-fective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We con-duct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and effi-ciency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly. |
| File Format | |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Content Type | Text |
