A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Yu, Yonghong; Qian, Weiwen; Zhang, Li; Gao, Rong

doi:10.3390/s22197122

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Fan et al [24] introduce a novel recommendation framework, named Relation-Enhanced Multiple Graph Attention Network (REMAN for short), which encodes user behavior and item knowledge into a unified relational graph for modeling heterogeneous and higher-order relationships between entities in recommendations. Yu et al [25] propose a social recommendation model based on a graph neural network, leveraging the GNN framework to capture higher-order collaborative signals, which are explicitly injected into the final representation of entities. He et al [13] simplify the structure of GCN by removing feature transformations and nonlinear transformations, constructing a lightweight graph convolution approach to propagate the embeddings of users and items, and stack a multi-layer graph convolutional network to capture the feature information of higher-order neighbors.…”

Section: Graph Neural Networkmentioning

confidence: 99%

RKR-GAT: Recurrent Knowledge-aware Recommendation with Graph Attention Network

Sun,

Wang,

Gong

2024

Preprint

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Knowledge graph-based recommender systems have attracted increasing attention in recent years. By extracting the semantics of entities and relationships, these recommender systems enable a more comprehensive understanding of user preferences. However, existing methods overlook the impact of low-quality links in knowledge graphs, which leads to user preference propagation bias and affects recommendation accuracy. In this paper, we propose RKR-GAT, a recurrent knowledge-aware recommendation algorithm based on a graph attention network. Specifically, RKR-GAT integrates user-item interactions and the knowledge graph into a heterogeneous graph and learns node embeddings using a Graph Attention Network (GAT). To alleviate the effect of user preference propagation bias, we design a new path filtering module to reject low-quality connections and adaptively retain reasonable reasoning paths between users and items in the knowledge graph. Furthermore, RKR-GAT combines recurrent neural networks with a self-attention mechanism to efficiently analyze the semantics of the paths to generate more accurate recommendations while providing explainability. Ultimately, experimental results on three real-world datasets demonstrate the better performance of RKR-GAT compared to state-of-the-arts baselines.

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Section: Graph Neural Networkmentioning

confidence: 99%

RKR-GAT: Recurrent Knowledge-aware Recommendation with Graph Attention Network

Sun,

Wang,

Gong

2024

Preprint

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“…(3) DiffNet++ (2020): a social recommendation model using graph convolutional networks, by aggregating higher-order neighbors in the social relationship graph and item interaction graph, respectively, and by distinguishing the influence of neighbors on users with an attention mechanism. (4) Light_NGSR [30] (2022): a social recommendation model based on the GNN framework, which retains only the neighborhood aggregation component and drops the feature transformation and nonlinear activation components. It aggregates higherorder neighborhood information from user-item interaction graphs and social network graphs.…”

Section: Experimental Comparison Of Social Recommendation Modelsmentioning

confidence: 99%

A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks

Zhang,

Wang

et al. 2023

Entropy

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Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user–item interaction data. Therefore, how to effectively fuse interaction information and social information becomes a hot research topic in social recommendation, and how to mine and exploit the heterogeneous information in the interaction and social space becomes the key to improving recommendation performance. In this paper, we propose a social recommendation model based on basic spatial mapping and bilateral generative adversarial networks (MBSGAN). First, we propose to map the base space to the interaction and social space, respectively, in order to overcome the issue of heterogeneous information fusion in two spaces. Then, we construct bilateral generative adversarial networks in both interaction space and social space. Specifically, two generators are used to select candidate samples that are most similar to user feature vectors, and two discriminators are adopted to distinguish candidate samples from high-quality positive and negative examples obtained from popularity sampling, so as to learn complex information in the two spaces. Finally, the effectiveness of the proposed MBSGAN model is verified by comparing it with both eight social recommendation models and six models based on generative adversarial networks on four public datasets, Douban, FilmTrust, Ciao, and Epinions.

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“…Uses social network data such as user profiles, connections, and activity to make recommendations [101], [102].…”

Section: Functionality Socialmentioning

confidence: 99%

“…Deep learning has been applied to model the social network-enhanced collaborative filtering problem [206]. Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and improve the recommendation performance [101]. Wu et al [102] argue that neglecting the latent collaborative interests of users hidden in the user-item interest network by only modeling the influence diffusion process in the social network.…”

Section: F: Context-based Approaches For Enhanced Personalizationmentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Cited by 10 publications

References 24 publications

RKR-GAT: Recurrent Knowledge-aware Recommendation with Graph Attention Network

RKR-GAT: Recurrent Knowledge-aware Recommendation with Graph Attention Network

A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks

Deep Learning-Based Recommendation System: Systematic Review and Classification

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