用户兴趣模型和Apriori算法在大学生就业推荐中的应用研究

杨明 樊旭 徐浩然

摘  要： 為了实现大学生就业的智能推荐和兴趣匹配，提出基于用户兴趣模型和Apriori算法的大学生就业推荐模型。构建大学生就业的用户兴趣信息采集与大数据分布模型，采用大数据关联信息挖掘方法进行大学生就业的兴趣特征匹配，在关联规则约束控制下，构建大学生就业的兴趣相关性特征量，对大学生就业推荐的兴趣特征大数据进行优化融合处理。采用Apriori算法进行大学生就业推荐的兴趣特征点自适应匹配，通过模糊自适应寻优方法实现对大学生就业行为的优化推荐。仿真结果表明，采用该方法进行大学生就业推荐的可靠性较好，提高了大学生就业的满意度水平。

关键词： 用户兴趣模型; Apriori算法; 大学生就业推荐; 大数据优化融合处理; 特征点匹配; 自适应匹配

中图分类号： TN911.1?34; TP391                    文献标识码： A                   文章编号： 1004?373X（2020）13?0119?04

Research on application of user interest model and Apriori algorithm in employment recommendation of college students

YANG Ming， FAN Xu， XU Haoran

（Hebei University of Water Resources and Electric Engineering， Cangzhou 061001， China）

Abstract： A college students′ employment recommendation model based on the user interest model and Apriori algorithm is put forward to achieve the intelligent employment recommendation of college students and their interest matching. The user interest information collection and big data distribution model for college students′ employment is constructed， and then the big data association information mining method is used to match the interest characteristics of college students′ employment. The college student employment′s interest correlation characteristic quantity under the constraint control of association rules is constructed to optimize and fuse the big data of interest characteristics of college students′ employment recommendation. The Apriori algorithm is used to adaptively match the interest characteristic points of college students′ employment recommendation. The fuzzy adaptive optimization method is used to realize the optimal recommendation of college students′ employment behavior. The simulation results show that the proposed method is reliable in college students′ employment recommendation and improves the satisfaction level of college students′ employment.

Keywords： user interest model; Apriori algorithm; college students′ employment recommendation; big data optimization and fusion; feature point matching; adaptive matching