用戶興趣模型和Apriori算法在大學生就業推薦中的應用研究

楊明 樊旭 徐浩然

摘? 要： 為了實現大學生就業的智能推薦和興趣匹配，提出基于用戶興趣模型和Apriori算法的大學生就業推薦模型。構建大學生就業的用戶興趣信息采集與大數據分布模型，采用大數據關聯信息挖掘方法進行大學生就業的興趣特征匹配，在關聯規則約束控制下，構建大學生就業的興趣相關性特征量，對大學生就業推薦的興趣特征大數據進行優化融合處理。采用Apriori算法進行大學生就業推薦的興趣特征點自適應匹配，通過模糊自適應尋優方法實現對大學生就業行為的優化推薦。仿真結果表明，采用該方法進行大學生就業推薦的可靠性較好，提高了大學生就業的滿意度水平。

關鍵詞： 用戶興趣模型; Apriori算法; 大學生就業推薦; 大數據優化融合處理; 特征點匹配; 自適應匹配

中圖分類號： TN911.1?34; TP391? ? ? ? ? ? ? ? ? ? 文獻標識碼： 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