随着高校毕业生数量的增加、企业招聘信息的几何倍数增长和就业市场的竞争日益激烈,为毕业生提供个性化、有效的求职推荐方案变得尤为重要。本文设计并实现了一个面向高校毕业生的求职推荐系统。整个系统的开发基于B/S架构,选用J2EE平台...随着高校毕业生数量的增加、企业招聘信息的几何倍数增长和就业市场的竞争日益激烈,为毕业生提供个性化、有效的求职推荐方案变得尤为重要。本文设计并实现了一个面向高校毕业生的求职推荐系统。整个系统的开发基于B/S架构,选用J2EE平台,采用MySQL8.1数据库,将SpringBoot和MyBatis-Plus组合使用构筑应用程序。系统包括客户端、中间服务层、面向对象数据持久化层和后端的关系型数据库。整个系统分为前端与后端。后端主要实现对应业务的逻辑处理、对数据库的操作、与前端的交互和通信、用户认证、授权机制和日志记录等功能。前端实现主要采用Vue.js设计用户界面、实现用户与程序间的交互、从后端获取数据展示给用户和反馈数据给后端等功能。本系统不仅提供招聘信息展示平台,集中呈现最新招聘岗位,减少信息冗余,还通过智能算法为求职者提供个性化的职位推荐。这一推荐引擎能够根据用户的学历背景、兴趣、职业规划和行为数据,自动生成符合求职者需求的职位列表,大大提高了传统推荐的精准度和匹配度。系统还针对冷启动问题进行了创新设计,利用多维度的数据分析和社交网络信息,帮助求职者快速进入求职状态,减少初期信息缺失带来的影响。此外,本系统支持高并发、快速响应,具备优秀的扩展性,能够应对未来就业市场快速变化和满足大规模数据处理的需求。通过这些技术的创新结合,系统不仅能为毕业生提供个性化的求职推荐,还能通过智能化和数据驱动的方式提升求职效率,优化毕业生与企业的匹配度,为学生就业和企业招聘带来全新的价值。With the increasing number of college graduates, the exponential growth of job recruitment information, and the increasingly fierce competition in the job market, it has become particularly important to provide personalized and effective job recommendation solutions for graduates. In this thesis, a job recommendation system for college graduates was designed and implemented. The system development was based on the B/S architecture, with J2EE platform being selected and MySQL8.1 database being used. Spring Boot and MyBatis-Plus were combined to build the application. The system was combined with a client, a middle service layer, an object-oriented data persistence layer, and a relational database at the back end. The entire system was divided into the front end and the back end. At the back end, those functions were mainly implemented: the logical processing of corresponding business, database operations, interaction and communication with the front end, user authentication mechanism as well as authorization mechanism. Additionally, log recording functions were also performed at the back end. At the front end, Vue.js was mainly used to design user interface while realizing user interaction with program, retrieving data from the back end for display to users, and providing feedback of data to the back end. This system builds a job information display platform, which can centrally display job information, reduce information redundancy, and help job seekers to job hunt more efficiently and quickly. This system not only provides a recruitment information display platform, centrally presenting the latest job openings and reducing information redundancy, but also uses intelligent algorithms to offer personalized job recommendations for job seekers. The recommendation engine automatically generates a list of job positions that match the job seeker’s needs based on their educational background, interests, career plans, and behavioral data, significantly improving the accuracy and relevance of the recommendations. The system also innovatively addresses the cold-start problem by utilizing multidimensional data analysis and social network information, helping job seekers quickly transition into job-seeking mode and reducing the impact of initial information gaps. Furthermore, the system supports high concurrency, rapid response, and scalability, making it capable of handling the fast-changing employment market and large-scale data processing demands. Through the innovative integration of these technologies, the system not only provides personalized job recommendations for graduates, but also enhances job-seeking efficiency and optimizes the match between graduates and companies through intelligent and data-driven approaches, bringing new value to student employment and corporate recruitment.展开更多
文摘随着高校毕业生数量的增加、企业招聘信息的几何倍数增长和就业市场的竞争日益激烈,为毕业生提供个性化、有效的求职推荐方案变得尤为重要。本文设计并实现了一个面向高校毕业生的求职推荐系统。整个系统的开发基于B/S架构,选用J2EE平台,采用MySQL8.1数据库,将SpringBoot和MyBatis-Plus组合使用构筑应用程序。系统包括客户端、中间服务层、面向对象数据持久化层和后端的关系型数据库。整个系统分为前端与后端。后端主要实现对应业务的逻辑处理、对数据库的操作、与前端的交互和通信、用户认证、授权机制和日志记录等功能。前端实现主要采用Vue.js设计用户界面、实现用户与程序间的交互、从后端获取数据展示给用户和反馈数据给后端等功能。本系统不仅提供招聘信息展示平台,集中呈现最新招聘岗位,减少信息冗余,还通过智能算法为求职者提供个性化的职位推荐。这一推荐引擎能够根据用户的学历背景、兴趣、职业规划和行为数据,自动生成符合求职者需求的职位列表,大大提高了传统推荐的精准度和匹配度。系统还针对冷启动问题进行了创新设计,利用多维度的数据分析和社交网络信息,帮助求职者快速进入求职状态,减少初期信息缺失带来的影响。此外,本系统支持高并发、快速响应,具备优秀的扩展性,能够应对未来就业市场快速变化和满足大规模数据处理的需求。通过这些技术的创新结合,系统不仅能为毕业生提供个性化的求职推荐,还能通过智能化和数据驱动的方式提升求职效率,优化毕业生与企业的匹配度,为学生就业和企业招聘带来全新的价值。With the increasing number of college graduates, the exponential growth of job recruitment information, and the increasingly fierce competition in the job market, it has become particularly important to provide personalized and effective job recommendation solutions for graduates. In this thesis, a job recommendation system for college graduates was designed and implemented. The system development was based on the B/S architecture, with J2EE platform being selected and MySQL8.1 database being used. Spring Boot and MyBatis-Plus were combined to build the application. The system was combined with a client, a middle service layer, an object-oriented data persistence layer, and a relational database at the back end. The entire system was divided into the front end and the back end. At the back end, those functions were mainly implemented: the logical processing of corresponding business, database operations, interaction and communication with the front end, user authentication mechanism as well as authorization mechanism. Additionally, log recording functions were also performed at the back end. At the front end, Vue.js was mainly used to design user interface while realizing user interaction with program, retrieving data from the back end for display to users, and providing feedback of data to the back end. This system builds a job information display platform, which can centrally display job information, reduce information redundancy, and help job seekers to job hunt more efficiently and quickly. This system not only provides a recruitment information display platform, centrally presenting the latest job openings and reducing information redundancy, but also uses intelligent algorithms to offer personalized job recommendations for job seekers. The recommendation engine automatically generates a list of job positions that match the job seeker’s needs based on their educational background, interests, career plans, and behavioral data, significantly improving the accuracy and relevance of the recommendations. The system also innovatively addresses the cold-start problem by utilizing multidimensional data analysis and social network information, helping job seekers quickly transition into job-seeking mode and reducing the impact of initial information gaps. Furthermore, the system supports high concurrency, rapid response, and scalability, making it capable of handling the fast-changing employment market and large-scale data processing demands. Through the innovative integration of these technologies, the system not only provides personalized job recommendations for graduates, but also enhances job-seeking efficiency and optimizes the match between graduates and companies through intelligent and data-driven approaches, bringing new value to student employment and corporate recruitment.
