Graph Neural Networks in Recommender Systems: A Survey (2011.02260v4)

Abstract: With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.

• The paper presents a taxonomy that categorizes GNN-based recommender systems into five classes, including user-item collaborative filtering and sequential recommendation.
• The paper reports significant improvements, such as a 7–10% boost in NDCG with LightGCN and a 10% increase in MRR with SR-GNN on key datasets.
• The paper discusses both theoretical and practical implications, emphasizing scalability, robustness, and adaptive strategies for real-world, dynamic recommendation challenges.

An Expert Review of "Graph Neural Networks in Recommender Systems: A Survey"

The paper "Graph Neural Networks in Recommender Systems: A Survey" offers an exhaustive examination of the integration of Graph Neural Network (GNN) techniques into recommender systems. The paper presents a well-structured taxonomy to categorize the existing methodologies based on the type of information utilized and the specific recommendation tasks addressed. This essay provides an expert overview of the paper's contributions, focusing on critical numerical results, bold claims, practical and theoretical implications, and future AI developments.

Taxonomy and Categorization

The survey categorizes GNN-based recommender systems into five primary classes:

1. User-Item Collaborative Filtering: This class leverages GNNs to capture collaborative signals from user-item interactions.
2. Sequential Recommendation: Techniques in this category utilize GNNs to model sequential patterns in user behaviors.
3. Social Recommendation: Here, GNNs are employed to incorporate social relationships among users to enhance recommendation accuracy.
4. Knowledge Graph-based Recommendation: GNNs are applied to integrate semantic relationships among items.
5. Other Tasks: This includes specialized tasks such as Point-of-Interest (POI) recommendation, multimedia recommendation, and bundle recommendation.

Critical Numerical Results and Bold Claims

The paper's comprehensive review highlights various GNN models that achieve state-of-the-art performance across multiple datasets and recommendation tasks:

• In User-Item Collaborative Filtering, models like NGCF and LightGCN demonstrate superior performance on datasets like MovieLens and Amazon. For instance, LightGCN shows a 7-10% improvement in NDCG compared to its predecessors due to its simplified propagation and aggregation steps.
• For Sequential Recommendation, techniques such as SR-GNN and GCE-GNN significantly outperform RNN-based methods on metrics like MRR and HR. For example, SR-GNN shows a 10% increase in MRR on the Yoochoose dataset.
• Social Recommendation models like DiffNet++ leverage multi-hop social network information and graph attention mechanisms to yield notable improvements. On the Yelp dataset, DiffNet++ improves HR by 5% compared to non-GNN baselines.

Theoretical and Practical Implications

The integration of GNN techniques into recommender systems has both practical and theoretical implications:

Practical Implications:

• Enhanced Performance: GNN models, by leveraging graph structures, can better capture complex relationships among entities leading to improved recommendation accuracy.
• Flexibility: The unified framework of GNN allows models to integrate various types of information (e.g., social networks, knowledge graphs) seamlessly.
• Scalability: Techniques like sampling and graph partitioning enable GNNs to handle large-scale datasets efficiently.

Theoretical Implications:

• Generalizability: The ability of GNNs to learn from graph-structured data makes them applicable to a wider range of recommendation problems.
• Robustness: Despite their advantages, GNNs are sensitive to noise in data, prompting future research into robust models.
• Interpretability: The inclusion of GNNs introduces explainability challenges but also opportunities for more interpretable recommendations through graph structures.

Future Developments in AI

The survey points to several promising research directions in GNN-based recommender systems:

• Diverse and Uncertain Representations: Future models need to capture users’ multi-faceted interests and manage uncertainty in recommendations.
• Scalability: Developing more efficient sampling strategies and scalable GNN architectures will be crucial for handling large-scale recommendation tasks.
• Dynamic Graphs: Real-time updates in recommendations require models to adapt dynamically to evolving graph structures.
• Self-Supervised Learning: Leveraging self-supervised tasks to improve the utilization of data and alleviate the sparsity problem holds significant potential.
• Fairness, Privacy, and Robustness: Ensuring fairness, protecting user privacy, and increasing robustness against adversarial attacks are critical for deploying GNN-based recommender systems in real-world scenarios.
• Explainability: Making GNN recommendations interpretable will enhance user trust and facilitate debugging and improvement of models.

Conclusion

This survey is a significant contribution to understanding the integration of GNNs in recommender systems. It provides a detailed analysis of current models and outlines future research directions that are likely to advance the field further. The theoretical and practical insights offered in this paper are crucial for researchers and practitioners aiming to develop next-generation recommender systems.