Paradigm Shifts in Team Recommendation: From Historical Subgraph Optimization to Emerging Graph Neural Network
Mahdis Saeedi, Christine Wong, Hossein Fani · 2024
Collaborative team recommendation involves selecting experts with certain skills to form a team who will, more likely than not, accomplish a task successfully.To automate the traditionally tedious and error-prone manual process of team formation, researchers from several scientific spheres have proposed methods to tackle the problem.In this tutorial, while providing a taxonomy of team recommendation works based on their algorithmic approaches, we foremost perform a comprehensive study of the graph-based approaches that comprise the pioneering works in this field, then cover the graph neural network-based studies as the cutting-edge class of approaches.Further, we provide unifying definitions, formulations, and evaluation schema along with the details of training strategies, benchmarking datasets, useful open-source tools and performance comparison of the works.Finally, we identify directions for future works.Our tutorial and materials are available at https://fani-lab.github.io/OpeNTF/tutorial/sigir-ap24/.