User behavior analysis of cloud service based on graph neural network research on personalized recommendation algorithm

Yao Sun

doi:10.1117/12.3026964

8 April 2024 User behavior analysis of cloud service based on graph neural network research on personalized recommendation algorithm

Yao Sun

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130904U (2024) https://doi.org/10.1117/12.3026964
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Cloud service recommendation is designed to recommend potential services of interest to users, which effectively alleviates the problems of data sparsity and cold startup. However, the existing recommendation algorithms mainly focus on the study of the single attribute relationship, and it is difficult to fully participate in the calculation of multiple attributes, and there are problems such as failure to make reasonable use of the heterogeneous relationship of services and low quality of node feature representation. Therefore, a Cloud Service Recommendation model Based on Attention and Convolution (ATCN) of heterogeneous relationships is proposed. ATCN uses graph convolutional network to aggregate user and service node information to generate query and embed to query node information. A neighbor sampling strategy combining the sampling probability with the consistency score of neighbor nodes is used to explore the heterogeneous relationships. A self-attention mechanism is used to aggregate node information to improve the representation quality of user and service characteristics. Experiments on two real data sets show that the proposed algorithm is significantly improved compared with the benchmark algorithm in MAE and RMSE.

Citation Download Citation

Yao Sun "User behavior analysis of cloud service based on graph neural network research on personalized recommendation algorithm", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130904U (8 April 2024); https://doi.org/10.1117/12.3026964

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