软件导刊2025，Vol.24Issue(2)：26-32,7.DOI:10.11907/rjdk.241043
基于特征融合的虚假招聘广告检测模型
Feature Fusion-Based Model for Fake Job Advertisement Detection
摘要
Abstract
Online recruitment has gradually replaced traditional offline recruitment and has become the preferred way for job seekers,but the emergence of false recruitment advertisements has brought great trouble to enterprises and job seekers,and seriously hindered the healthy de-velopment of online recruitment.In order to solve the problems of low detection accuracy and poor time efficiency of existing single machine learning models based on deep learning models,a false recruitment advertisement detection model based on feature fusion was proposed.First-ly,the attention mechanism is introduced to assign weights to each base classifier.Secondly,multiple base classifier features were horizontally fused to significantly improve the detection effect of fake recruitment advertisements.Experiments show that the accuracy of the feature fusion model is 1.78%,1.67%and 1.16%higher than that of the machine learning model,BERT deep learning model and TextCNN deep learning model,respectively,and the running time of the model is slightly higher than that of the machine learning models but much lower than that of the deep learning model.Experiments on the EMSCAD dataset show that the feature fusion model has good performance in detecting false re-cruitment advertisements.关键词
机器学习/特征融合/注意力机制/虚假招聘广告检测/深度学习Key words
machine learning/feature fusion/attention mechanism/fake job advertisement detection/deep learning分类
信息技术与安全科学引用本文复制引用
王永政,纪淑娟..基于特征融合的虚假招聘广告检测模型[J].软件导刊,2025,24(2):26-32,7.基金项目
国家自然科学基金项目(71772107) （71772107）
