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国内个性化推荐技术及其算法与数据风险的研究现状和趋势

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分析传统个性化推荐模型演化图谱和基于深度学习的个性化推荐模型演化图谱,对梳理个性化推荐技术的发展脉络具有重要意义.本研究采用CiteSpace 6.1.R2软件对中国知网(CNKI)上检索到的2002—2022年以"个性化推荐"为研究主题的中文文献和2005-2022年"个性化推荐算法与数据风险"相关文献进行分析,发现个性化推荐领域研究热点主题和未来发展趋势,对个性化推荐算法的数据风险与防范提出规制对策.
Research Trends of Personalized Recommendation Technology and Its Algorithm and Data Risk in China
Analyzing the evolution map of traditional personalized recommendation model and the evolution map of personalized recommendation model based on deep learning is of great significance to sort out the development context of personalized recommendation technology. This study used CiteSpace 6.1.R2 software to analyze Chinese literature on "personalized recommendation" retrieved from China National Knowledge Infrastructure (CNKI) from 2002 to 2022,as well as relevant literature on "personalized recommendation al-gorithms and data risks" from 2005 to 2022. The study identified hot topics and future development trends in the field of personalized recommendation,and proposed regulatory measures for data risks and prevention of personalized recommendation algorithms.

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王鹤琴、朱珍元

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安徽警官职业学院,安徽 合肥 230031

个性化推荐 算法 数据 风险

2024

安徽警官职业学院学报
安徽警官职业学院

安徽警官职业学院学报

影响因子:0.139
ISSN:1671-5101
年,卷(期):2024.23(4)