楊明 樊旭 徐浩然

摘? 要： 為了實(shí)現(xiàn)大學(xué)生就業(yè)的智能推薦和興趣匹配，提出基于用戶興趣模型和Apriori算法的大學(xué)生就業(yè)推薦模型。構(gòu)建大學(xué)生就業(yè)的用戶興趣信息采集與大數(shù)據(jù)分布模型，采用大數(shù)據(jù)關(guān)聯(lián)信息挖掘方法進(jìn)行大學(xué)生就業(yè)的興趣特征匹配，在關(guān)聯(lián)規(guī)則約束控制下，構(gòu)建大學(xué)生就業(yè)的興趣相關(guān)性特征量，對大學(xué)生就業(yè)推薦的興趣特征大數(shù)據(jù)進(jìn)行優(yōu)化融合處理。采用Apriori算法進(jìn)行大學(xué)生就業(yè)推薦的興趣特征點(diǎn)自適應(yīng)匹配，通過模糊自適應(yīng)尋優(yōu)方法實(shí)現(xiàn)對大學(xué)生就業(yè)行為的優(yōu)化推薦。仿真結(jié)果表明，采用該方法進(jìn)行大學(xué)生就業(yè)推薦的可靠性較好，提高了大學(xué)生就業(yè)的滿意度水平。

關(guān)鍵詞： 用戶興趣模型; Apriori算法; 大學(xué)生就業(yè)推薦; 大數(shù)據(jù)優(yōu)化融合處理; 特征點(diǎn)匹配; 自適應(yīng)匹配

中圖分類號： TN911.1?34; TP391? ? ? ? ? ? ? ? ? ? 文獻(xiàn)標(biāo)識碼： A? ? ? ? ? ? ? ? ? ?文章編號： 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