摘要
为了解决目前就业推荐方法资源覆盖率低的问题,设计一种基于人工智能技术的智媒体就业推荐平台。通过人工智能技术设计智媒体就业推荐平台的整体框架,包括基础数据模块、用户界面模块、后台管理模块及个性化推荐模块,并设计学生和管理者在智媒体就业推荐平台中的业务流程。采用基于就业意向的个性化推荐算法,在智媒体就业推荐平台中为用户推荐就业资源,完成智媒体就业推荐平台的构建。实验结果表明,该平台运行时间随着请求数量的增加缓慢上升,抗压性较好,资源覆盖率均高于80%。
In order to solve the problem of low resource coverage of current employment recommendation methods,a smart media employment recommendation platform based on artificial intelligence technology is designed.The overall framework of the smart media employment recommendation platform is designed through artificial intelligence technology,including basic data modules,user interface modules,background management modules and personalized recommendation modules,and the business process of students and managers in the smart media employment recommendation platform is designed.It adopts a personalized recommendation algorithm based on employment intentions,recommends employment resources for users in the smart media employment recommendation platform,and completes the construction of the smart media employment recommendation platform.The experimental results show that the running time of the platform slowly rises with the increase of the number of requests,the stress resistance is better,and the resource coverage rate is higher than 80%.
作者
赵勇进
ZHAO Yong-jin(Xi'an Polytechnic University,Xi'an 710048 China)
出处
《自动化技术与应用》
2024年第1期84-87,共4页
Techniques of Automation and Applications
关键词
改进深度学习网络
特征提取
D-S证据理论
分类模型
就业推荐
improving the deep learning network
feature extraction
D-S evidence theory
classification model
employment recommendation