温馨提示本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片温馨提示本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片温馨提示本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片技术范围SpringBoot、Vue、爬虫、数据可视化、小程序、安卓APP、大数据、知识图谱、机器学习、Hadoop、Spark、Hive、大模型、人工智能、Python、深度学习、信息安全、网络安全等设计与开发。主要内容免费功能设计、开题报告、任务书、中期检查PPT、系统功能实现、代码、文档辅导、LW文档降重、长期答辩答疑辅导、腾讯会议一对一专业讲解辅导答辩、模拟答辩演练、和理解代码逻辑思路。本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片感兴趣的可以先收藏起来还有大家在毕设选题项目以及LW文档编写等相关问题都可以给我留言咨询希望帮助更多的人信息安全/网络安全 大模型、大数据、深度学习领域中科院硕士在读所有源码均一手开发感兴趣的可以先收藏起来还有大家在毕设选题项目以及论文编写等相关问题都可以给我留言咨询希望帮助更多的人介绍资料随着互联网和信息技术的不断发展就业和招聘形式也迎来了深刻的变革。就业推荐系统不仅能够为求职者提供个性化、精准的匹配而且还解决了现代就业市场面临的一系列挑战问题另外对于求职者而言该系统还减轻了信息过载的压力帮助毕业生快速找到适合自己技能和兴趣的职位节省了毕业生的求职时间和成本提高了就业市场的效率同时为毕业生提供更好的就业机会和就业支持。本文基于协同过滤算法设计实现了就业推荐系统旨在提供高度个性化的职位推荐以满足毕业生求职者和雇主的需求。系统首先采用爬虫技术收集企业公开发布的招聘岗位信息抽取特征值并且归一化处理根据指定的毕业生和求职人员的相似度计算毕业生和企业单位的相似度。然后在得到毕业生和企业单位的相似度的基础上根据随机游走模型算法计算出企业单位的求职热度。最后计算最终的排序综合权值完成基于协同过滤的就业推荐系统的建立。该系统涵盖了控制台功能、数据爬取、数据管理、数据可视化和就业推荐等多个核心功能模块。同时系统为解决就业场景中企业与毕业生相互匹配的问题引入企业对毕业生的偏好系数企业偏好系数是利用改进的随机游走算法PersonalRank计算企业招聘过程中对毕业生特征属性的招聘偏好值。最终的推荐算法融合企业偏好系数计算出毕业生与企业的符合度根据符合度为毕业生推荐合适的就业岗位。通过该系统毕业生用户可以轻松地管理其就业信息获得定制的职位推荐并通过可视化工具了解就业市场趋势。系统的设计过程中使用了先进的技术和开发框架确保了系统的高性能和可扩展性。整个系统采用 Python 语言编写后端基于 Django 的 Web 应用框架数据库采用 MySQL设计使用 ECharts 进行数据可视化显示。数据获取使用 Selenium 框架进行数据的采集然后对数据进行分析并且将结果在前台进行可视化的展示。获取的数据解析后存储到数据库。该系统的个性化就业职位推荐功能是基于用户的协同过滤算法设计实现。经过功能测试和性能测试系统展现出了其在多个方面的有效性和可靠性。总之本文开发的就业推荐系统为解决现代就业市场的智能化挑战提供了一个有前景的解决方案并为未来功能更加强大的就业推荐系统的研究和应用提供了一定的基础。关 键 词协同过滤算法就业推荐系统可视化系统Django框架职位推荐论文类型应用研究ABSTRACTWith the continuous development of the Internet and information technology, employment and recruitment forms have also undergone profound changes. The employment recommendation system not only provides personalized and accurate matching for job seekers, but also solves a series of challenges faced by the modern job market. In addition, for job seekers, the system also reduces the pressure of information overload, helps graduates quickly find positions suitable for their skills and interests, saves graduates job search time and costs, and improves the efficiency of the job market, At the same time, provide better employment opportunities and support for graduates.This article designs and implements an employment recommendation system based on collaborative filtering algorithms, aiming to provide highly personalized job recommendations to meet the needs of graduate job seekers and employers. The system first uses crawler technology to collect publicly released recruitment position information from enterprises, extract feature values, and normalize them. Based on the similarity between designated graduates and job seekers, the similarity between graduates and enterprise units is calculated. Then, based on the similarity between graduates and business units, the job search enthusiasm of the business unit is calculated using the random walk model algorithm. Finally, calculate the final ranking comprehensive weights and complete the establishment of an employment recommendation system based on collaborative filtering. The system covers multiple core functional modules, including console functions, data crawling, data management, data visualization, and job recommendation. At the same time, in order to solve the problem of matching between enterprises and graduates in employment scenarios, the system introduces the preference coefficient of enterprises for graduates. The preference coefficient of enterprises is calculated using an improved Personal Rank algorithm to calculate the recruitment bias value of student feature attributes in the recruitment process of enterprises. The final recommendation algorithm combines the preference coefficient of the enterprise to calculate the degree of conformity between graduates and the enterprise, and recommends suitable employment positions for graduates based on the degree of conformity. Through thissystem, graduate users can easily manage their employment information, obtain customized job recommendations, and understand job market trends through visual tools.Advanced technology and development frameworks were used in the design process of the system, ensuring its high performance and scalability. The entire system is written in Python language, with the backend based on Djangos web application framework. The database is designed using MySQL, and ECharts is used for data visualization display. The Selenium framework is used for data collection, followed by data analysis and visualization of the results in the front-end. The obtained data is parsed and stored in the database. The personalized job recommendation function of this system is designed and implemented based on user collaborative filtering algorithms. After functional and performance testing, the system has demonstrated its effectiveness and reliability in multiple aspects. In summary, the employment recommendation system developed in this article provides a promising solution to the intelligent challenges of the modern job market, and lays a certain foundation for the research and application of more powerful employment recommendation systems in the future.Key words: Collaborative filtering algorithmemployment recommendation systemvisualization systemDjango frameworkjob recommendationThesis:ApplicationStudy目 录第一章 绪论1研究背景1国内外研究现状1国外研究现状1国内研究现状2研究目的及意义3研究内容3论文结构安排4第二章 关键技术5就业推荐系统概述5基于用户的协同过滤算法5基于物品的协同过滤算法7相似度计算模型7欧氏距离8余弦距离11皮尔逊相关系数11Django 框架14Selenium 框架14本章小结15第三章 协同过滤推荐算法模型16推荐原理16本章小结26第四章 系统分析与设计27系统需求分析27功能需求分析27非功能性需求分析28系统功能结构设计28数据库设计29数据库概念结构设计29数据表设计32本章小结34第五章 就业推荐系统实现35控制台功能功能实现35数据爬取功能实现36数据管理功能实现38数据可视化功能实现40就业推荐功能实现42本章小结45第六章 系统测试46测试目的46系统功能测试46系统性能测试49测试结果分析50本章小结51第七章 总结与展望52总结52展望52参考文献54运行截图推荐项目上万套Java、Python、大数据、机器学习、深度学习等高级选题(源码lw部署文档讲解等)项目案例优势1-项目均为博主学习开发自研适合新手入门和学习使用2-所有源码均一手开发不是模版不容易跟班里人重复为什么选择我博主是CSDN毕设辅导博客第一人兼开派祖师爷、博主本身从事开发软件开发、有丰富的编程能力和水平、累积给上千名同学进行辅导、全网累积粉丝超过50W。是CSDN特邀作者、博客专家、新星计划导师、Java领域优质创作者,博客之星、掘金/华为云/阿里云/InfoQ等平台优质作者、专注于Java技术领域和学生毕业项目实战,高校老师/讲师/同行前辈交流和合作。✌感兴趣的可以先收藏起来点赞关注不迷路想学习更多项目可以查看主页大家在毕设选题项目代码以及论文编写等相关问题都可以给我留言咨询希望可以帮助同学们顺利毕业✌源码获取方式由于篇幅限制获取完整文章或源码、代做项目的本人主页置顶文章(点我)开头有 CSDN 平台官方提供的学长联系方式的名片。点赞、收藏、关注不迷路