Matrix Decomposition Recommendation Algorithm Based on Multiple Social Relationships

Gong, Cuijuan; Sun, Gengxin; Chen, Chi-Cheng; Bin, Sheng

doi:10.1109/ecbios50299.2020.9203754

Cited by 1 publication

(1 citation statement)

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“…The social information is interpretable and can well reflect the preferences of users and other users. Therefore, social recommendations are proposed, which use the direct social relationship between users as auxiliary information to alleviate the sparsity of the recommendation system [9][10][11][12][13][14][15]. However, most of the existing algorithms use traditional methods, such as matrix decomposition to model, and these models are not deep enough to express users' collaborative interests and social impact [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

Liu

et al. 2023

Electronics

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Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user social information to alleviate this problem. Existing social recommendation methods cannot express the user interaction interest and social influence deeply, which limits the recommendation performance of the system. To address this problem, in this paper we propose a graph neural network social recommendation algorithm integrating multi-head attention mechanism. First, based on the user-item interaction graph and social network graph, the graph neural network is used to learn the high-order relationship between users and items and deeply extract the latent features of users and items. In the process of learning user embedding vector representation based on the social network graph, the multi-head attention mechanism is introduced to increase the importance of friends with high influence. Then, we make rating predictions for the target users according to the learned user embedding vector representation and item embedding vector. The experimental results on the Epinions dataset show that the proposed method outperforms the existing methods in terms of both Recall and Normalized Discounted Cumulative Gain.

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Section: Introductionmentioning

confidence: 99%

A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

Liu

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

Matrix Decomposition Recommendation Algorithm Based on Multiple Social Relationships

Cited by 1 publication

References 3 publications

A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

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