Graph Neural Networks for Social Recommendation

By Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, Dawei Yin. The Web Conference, 2019

2,365 citations recorded by the source corpus.

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/398d6f4432e6aa7acf21c0bbaaebac48998faad3

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. Xiangnan He
2. Tat-seng Chua
3. Xiang Wang
4. Jure Leskovec
5. Meng Wang
6. Liqiang Nie
7. Philip S. Yu
8. Xing Xie

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Meng Wang
5. Xing Xie
6. Liqiang Nie
7. Hongzhi Yin
8. Yong Li

Citations in the corpus

• A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions (2024, methodology): “Notice that the computation graph of the GNN forms a tree structure by unfolding the neighborhood around the target node [17].”
• Recommender Systems in the Era of Large Language Models (LLMs) (2023, positive): “Considering users’ online behaviors ( e.g., chick, purchase, socializing) as graph-structured data, Graph Neural Networks (GNNs) have emerged as powerful representation learning techniques to learn user and item representations [1], [11].”
• Identity-aware Graph Neural Networks (2021, background): “…discovery (You et al. 2018a,b; Jin, Barzilay, and Jaakkola 2018), recommender systems (Ying et al. 2018; You et al. 2019a), and network analysis (Kumar, Cheng, and Leskovec 2017; Morris et al. 2019; Fan et al. 2019; Ying et al. 2019) stand to be greatly impacted by the use and development of GNNs.”
• Graph Convolutional Networks with EigenPooling (2019, neutral): “Graph neural networks have also been extended to different types of graphs [9, 26, 27] and applied to various applications [12, 28, 35, 41, 45, 47].”
• Knowledge Graphs: Opportunities and Challenges (2023, background): “A representative example is GraphRec (a graph neural network framework for social recommendations) proposed by Fan et al. (2019).”
• Sequential Recommendation with Graph Neural Networks (2021, methodology): “GCN-based methods have achieved the state-of-the-art performance in other recommendation problems, such as social recommendation [6, 32, 40], knowledge graph-based recommendation [27, 28], multi-behavior recommendation [12], bundle recommendation [3], etc.”
• LLMRec: Large Language Models with Graph Augmentation for Recommendation (2023, background): “In modern recommender systems, such as Netflix, the side information available exhibits heterogeneity, including item attributes [44], user-generated content [8], and multi-modal features [43] encompassing both textual and visual aspects.”
• Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction (2020, background): “Graph convolutions [27,24,9,15,47] have demonstrated significant improvement on graph machine learning tasks, e.g., modeling physical systems [4,28], drug prediction [31] and social recommendation systems [11].”
• A federated graph neural network framework for privacy-preserving personalization (2021, methodology): “Fan et al. [5] proposed a social recommendation approach named GraphRec.”
• Multi-behavior Recommendation with Graph Convolutional Networks (2020, methodology): “Similar to the existing graph models in [9, 31, 42, 43], we use embedding vector p(0) i ∈ Rd and q (0) j ∈ Rd , where d is embedding size, to describe a user and an item.”
• AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System (2020, methodology): “SRGNN is the classical implementation to use GNN on recommendation, which represents the GNN-based recommender system such as GCSAN [77], GraphRec [15] and PinSage [78].”
• Structure-Aware Transformer for Graph Representation Learning (2022, background): “Copyright 2022 by the author(s). with successful applications in drug discovery (Gaudelet et al., 2021), protein design (Ingraham et al., 2019), social network analysis (Fan et al., 2019), and so on.”
• Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2020, background): “More recently, inspired by the success of graph convolutional networks (GCNs) [15, 18, 20, 24, 32, 39, 41], some efforts [7, 12, 35, 36, 40, 43] have been made to organize user behaviors as a bipartite user-item graph and integrate multi-hop”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “Instead of performing GNNs on the user-user social graph, researchers have also considered jointly modeling the social diffusion process in the social network and the interest diffusion process in the user-item graph with heterogeneous GNN based models [158], [161], [162], [163],”
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, background): “…attributed graphs), where nodes with attributes indicate real-world entities and links indicate the relationship between entities, are ubiquitous in various scenarios, including finance (trading networks) [1], social media (social networks) [2], [3], and e-commerce (item-user networks) [4], [5].”
• Next-item Recommendation with Sequential Hypergraphs (2020, background): “In social recommendation, social connections between users can be investigated with GNN to model the propagation of user preference in social networks [10, 36, 40].”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “Recently, Fan et al. [132] used the GNN to represent a user-user social network and a user-item interaction graph separately, which outperforms traditional CF-based recommender systems with user social information.”
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation (2020, methodology): “GraphRec further improves over these traditional social recommendation models by jointly considering the first-order social neighbors and interest neighbors in the user embedding process.”
• Knowledge Graph Contrastive Learning for Recommendation (2022, background): “However, even with the modeling of complex user-item interaction patterns, most CF-based recommendation methods still suffer from the data scarcity issue for users who have not yet interacted with sufficient items [7, 14, 31].”