Transfer Learning with Graph Attention Networks for Team Recommendation

In order to complete a common goal, team recommendation problems identify an efficient group of experts who can collectively satisfy a set of required skills. A significant number of studies address this problem through graph-based approaches. Recently, researchers have started to see this problem as a social information retrieval and examine it through neural architectures that recommend the team of experts by learning a relationship between the skills and experts space. However, this learning process faces several challenges including (1) being unable to handle the modification of a network if the training process is over, (2) the time complexity of the learning process being high and proportional to the size of the network. In this paper, we propose a new architecture, LANT, which comprises transfer learning and neural team recommendation, to address these challenges based on graph neural networks and variational inference. Since the transfer learning of team recommendation is an unsupervised task, therefore, to learn node embedding in a self-supervised manner, we use Deep Graph Infomax with Graph Attention Networks as an encoder. We empirically demonstrate how LANT overcomes the challenges in the existing approaches and compare them against the state-of-the-art approaches in terms of effectiveness using the DBLP dataset.