A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2109.12843v3)

Abstract: Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories, spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data, and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in \url{https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems}.

Overview of "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions"

The paper "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions" presents a detailed examination of the integration of graph neural networks (GNNs) in the field of recommender systems. With the continual evolution of recommender systems from shallow models to those leveraging deep learning, GNNs have recently emerged as a state-of-the-art approach. This paper systematically addresses the complexities, methodologies, and future pathways for incorporating GNNs into recommendation tasks.

Key Challenges and Methodologies

Graph Construction

One significant challenge addressed in the paper is the construction of graphs suitable for specific recommendation tasks. The authors highlight the necessity of defining graph nodes and edges carefully, considering aspects such as graph density and structural complexity. For example, in collaborative filtering tasks, representing users and items as nodes with interactions as edges can provide a foundational structure for constructing effective input graphs. The paper emphasizes managing graph density to optimize computational feasibility and performance.

Propagation and Aggregation

The paper explores the design of the propagation and aggregation mechanisms within GNNs. These components are critical for capturing high-order similarities and interactions in recommendation systems. The choice between spectral and spatial models, as well as the selection of appropriate aggregation functions like mean pooling or LSTM, plays a central role in model performance and efficiency. The design must also consider the depth of propagation layers to balance information richness and the risk of over-smoothing.

Model Optimization

Optimizing GNN-based models for recommendation introduces unique challenges, particularly in leveraging both traditional loss functions and graph-structure-informed sampling techniques. The integration of multi-task learning frameworks to handle tasks with varying objectives is also explored, highlighting the complexity of model optimization within the context of GNNs.

Computational Efficiency

Scalability and efficiency remain at the forefront of deploying GNNs in real-world recommender systems. The paper discusses various strategies, such as sampling and pruning, to address computational constraints, especially when dealing with large-scale graphs typical of industrial applications.

Practical and Theoretical Implications

The paper demonstrates the broad applicability of GNNs across different recommendation stages, including matching, ranking, and re-ranking, as well as diverse scenarios like social, sequential, and cross-domain recommendations. It also examines objectives beyond mere accuracy, such as diversity, explainability, and fairness. By leveraging the structural properties of data and advanced representation learning capabilities, GNNs present opportunities to enhance the granularity and personalization of recommendations significantly.

Future Directions

The authors propose several future directions for research. These include the development of deeper GNN architectures to capture higher-order connectivity without encountering over-smoothing and the potential of dynamic GNN models for handling time-variant data in continuously evolving recommendation contexts. The integration of self-supervised learning to enhance representation robustness and the exploration of automated machine learning techniques to tailor GNN architectures for specific recommendation scenarios are also suggested.

Conclusion

This paper offers a comprehensive overview of how graph neural networks can be effectively integrated into recommender systems, overcoming traditional challenges and opening new avenues for innovation. Its exploration of methodologies and future pathways establishes a solid foundation for ongoing research and application development within the field. The work underscores the transformative potential of GNNs in advancing the state-of-the-art in recommender systems, emphasizing both the current capabilities and future possibilities afforded by this approach.