科研成果详情

题名Design of a Graduate Employment Intelligent Recommendation System Based on Data Analysis Algorithms
作者
发表日期2023-11-30
会议名称3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023
会议录名称Frontiers in Artificial Intelligence and Applications
ISSN0922-6389
卷号373
页码183-192
会议日期30 November 2023
会议地点Hybrid, Zhengzhou
摘要

In response to the issues of indiscriminate employment and lengthy contract signing time for university graduates, this research investigates the relationship between graduate behavior and job demand. Taking the past graduates of several universities in Hubei province, China as the research subjects, this study proposes an improved algorithm, Apriori-S, based on the Spark computing framework, to enhance the execution efficiency and applicability of the original Apriori algorithm for analyzing complex datasets. The improved algorithm is applied to mine the association rules between past graduate data and employment information. Experimental analysis demonstrates that the improved algorithm has significantly higher execution efficiency compared to the original algorithm and is suitable for analyzing complex datasets with multiple dimensions. Finally, based on the mining results, an 'employment stickiness' model is constructed, and a design for an intelligent job recommendation system for graduates is developed using data analysis algorithms, enabling intelligent job recommendations for employment positions.

关键词algorithm optimization Apriori algorithm complex dataset intelligent recommendation stickiness model
DOI10.3233/FAIA230808
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语种英语English
Scopus入藏号2-s2.0-85181396769
引用统计
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11104
专题北师香港浸会大学
通讯作者Jiang, Xiaolin
作者单位
1.School of Cloud Computing Technology and Application Industry, Shandong Institute of Commerce and Technology, Jinan, Shandong, China
2.Beijing Normal University-HongKong Baptist University United International College, Zhuhai, Guangdong, China
推荐引用方式
GB/T 7714
Jiang, Xiaolin,Wang, Ruiyang. Design of a Graduate Employment Intelligent Recommendation System Based on Data Analysis Algorithms[C], 2023: 183-192.
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