Beyond Searching a Village: Learning to Recommend Diverse and Successful Collaborative Teams
TL;DR
This tutorial discusses team refinement, which involves structural adjustments or expert replacements to enhance team performance in dynamic environments, and deliberately focuses on the recent learning-based approaches, with a particular in-depth exploration of graph neural network-based methods.
Abstract
Team recommendation involves selecting skilled experts to form an almost surely successful collaborative team, or refining the team composition to maintain or excel at performance. To address the tedious and error-prone manual process, computational approaches have been proposed, especially for web-scale social networks and widespread online collaboration and diversity of interactions. In this tutorial, with a brief overview of pioneering subgraph optimization approaches and their shortfalls, we deliberately focus on the recent learning-based approaches, with a particular in-depth exploration of graph neural network-based methods. More importantly, we then discuss team refinement, which involves structural adjustments or expert replacements to enhance team performance in dynamic environments. Finally, we discuss training strategies, benchmarking datasets, and open-source libraries, along with future research directions and real-world applications. Further resources are available at https://fani-lab.github.io/OpeNTF/tutorial/recsys26.