Retrieving Skill-Based Teams from Collaboration Networks

Given a set of required skills, the objective of the team formation problem is to form a team of experts that cover the required skills. Most existing approaches are based on graph methods, such as minimum-cost spanning trees. These approaches, due to their limited view of the network, fail to capture complex interactions among experts and are computationally intractable. More recent approaches adopt neural architectures to learn a mapping between the skills and experts space. While they are more effective, these techniques face two main limitations: (1) they consider a fixed representation for both skills and experts, and (2) they overlook the significant amount of past collaboration network information. We learn dense representations for skills and experts based on previous collaborations and bootstrap the training process through transfer learning. We also propose to fine-tune the representation of skills and experts while learning the mapping function. Our experiments over the DBLP dataset verify that our proposed architecture is able to outperform the state-of-the-art graph and neural methods over both ranking and quality metrics.