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Personalized agricultural knowledge services: a framework for privacy-protected user portraits and efficient recommendation

文献类型： 外文期刊

作者： Wu, Huarui 1 ; Liu, Chang 1 ; Zhao, Chunjiang 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Digital Village Technol, Beijing, Peoples R China

关键词： User portrait; Privacy protection; Knowledge service; Intelligent recommendation; Service matching

期刊名称：JOURNAL OF SUPERCOMPUTING （ 影响因子：3.3； 五年影响因子：3.0 ）

ISSN： 0920-8542

年卷期： 2023 年

页码：

收录情况： SCI

摘要： In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. This paper presents a comprehensive framework that addresses the issues of vague user positioning, serious privacy leakage, and low efficiency in personalized knowledge services within the national agricultural knowledge intelligent service cloud platform. The proposed framework utilizes privacy-protected user portraits based on generative adversarial nets (GAN) and leverages the TextCNN-LSTM algorithm for agricultural knowledge service prediction. By embedding labels into the algorithm and employing data obfuscation techniques, the framework achieves accurate inference of user behavior while preserving user privacy. Experimental results demonstrate the effectiveness and accuracy of the proposed framework, highlighting its potential for regional precise positioning and recommendation of personalized agricultural knowledge services. Experimental data shows that the average absolute error and root-mean-square error of this method are 1.1997 and 1.4143, respectively, and compared with MLP, TextCNN, and LSTM models, and it has higher prediction accuracy. In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services.

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