Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse and various in the real world, it is quite challenging to design proper GNN methods for specific problems. In this tutorial, we focus on the critical challenges of GNN-based recommendation and the potential solutions.

Specifically, we start from an extensive background of recommender systems and graph neural networks. Then we fully discuss why GNNs are required in recommender systems and the four parts of challenges, including graph construction, network design, optimization, and computation efficiency. Then, we discuss how to address these challenges by elaborating on the recent advances of GNN-based recommendation models, with a systematic taxonomy from four perspectives, stages, scenarios, objectives, and applications. Last, we finalize the tutorial with conclusions and discuss important future directions.

This tutorial targets a broad audience from academia and industry who are interested in recommender systems (RecSys) and graph neural networks. While we welcome participants with a relevant background to join our discussion, the tutorial should be of interest to any WSDM-community participants who want to learn about next-generation RecSys. As to prerequisites, the basic background of RecSys and GNN would be sufficient. Since we will introduce the basic concepts of RecSys and GNN, the background is not a strict requirement.