Graph Neural Networks in Recommender Systems: A Survey

Wu, Shiwen; Sun, Fengrui; Zhang, Wentao; Xie, Xiang; Cui, Bin

doi:10.48550/arxiv.2011.02260

Cited by 36 publications

(52 citation statements)

References 51 publications

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“…It provides extra information that may describe the items since items are likely to be similar or related [17]. At the same time, most of the information including social networks essentially has a graph structure, and graph neural networks (GNNs) have a powerful capability in graph representation learning, thus the field of utilizing GNNs in recommender systems is flourishing [30].…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

Lin¹,

Chen²,

Wang³

2022

Preprint

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Recommender systems based on graph neural networks receive increasing research interest due to their excellent ability to learn a variety of side information including social networks. However, previous works usually focus on modeling users, not much attention is paid to items. Moreover, the possible changes in the attraction of items over time, which is like the dynamic interest of users are rarely considered, and neither do the correlations among items. To overcome these limitations, this paper proposes graph neural networks with dynamic and static representations for social recommendation (GNN-DSR), which considers both dynamic and static representations of users and items and incorporates their relational influence. GNN-DSR models the short-term dynamic and long-term static interactional representations of the user's interest and the item's attraction, respectively. Furthermore, the attention mechanism is used to aggregate the social influence of users on the target user and the correlative items' influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GNN-DSR.

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Section: Introductionmentioning

confidence: 99%

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

Lin¹,

Chen²,

Wang³

2022

Preprint

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“…It is worth mentioning that there is one existing survey [168] of graph neural network-based recommender system. However, it is limited due to the following reasons.…”

Section: Introductionmentioning

confidence: 99%

“…Third, it does not explain the critical challenges of applying graph neural networks to recommendation and how to address them, which are fully discussed in this survey. Last, since this area is increasingly popular, our survey also introduces a lot of recently published papers not covered by [168].…”

Section: Introductionmentioning

confidence: 99%

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

Chen¹,

Zheng²,

Li³

et al. 2021

Preprint

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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 of recommender systems. In this survey, we conduct a comprehensive review of the literature in 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 of this area. We summarize the representative papers along with their codes repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems.

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“…vertex) represents an individual or entity, and each edge represents the corresponding connection or relation. Capturing graph structure of data is useful in many applications, such as targeted advertising [41,43], knowledge distillation [45,46], data annotation [47][48][49], and protein analysis [17,39]. Each node in a graph often exhibits a crucial property -the importance or utility of a node depends on the number of connections between it and other nodes.…”

Section: Introductionmentioning

confidence: 99%

K-Core Decomposition on Super Large Graphs with Limited Resources

Gao¹,

Xu²,

Li³

et al. 2021

Preprint

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K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-andconquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.

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Graph Neural Networks in Recommender Systems: A Survey

Cited by 36 publications

References 51 publications

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

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

K-Core Decomposition on Super Large Graphs with Limited Resources

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