Graph Neural Networks for Recommender System

By Chen Gao, Xiang Wang, Xiangnan He, Yong Li. Web Search and Data Mining, 2022

318 citations recorded by the source corpus.

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 critical perspectives: stages, scenarios, objectives, and applications. Last, we finalize this tutorial with conclusions and discuss important future directions.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/4fa31616b834c377c4995c346a2b17464f25692a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Tong Chen
  2. Hongzhi Yin
  3. Xin Xia
  4. Junliang Yu
  5. Li-zhen Cui
  6. Nguyen Quoc Viet Hung

In Information Systems

  1. Tong Chen
  2. Hongzhi Yin
  3. Xin Xia
  4. Junliang Yu
  5. Li-zhen Cui
  6. Nguyen Quoc Viet Hung

Citations in the corpus