In recent years, with the rapid development of higher education in China, the number of graduates has continued to increase significantly. The country attaches great importance to the employment of college graduates. The report of the 17th National Congress of the Communist Party of China clearly puts forward the overall requirement of "implementing the strategy of prioritizing education development and actively doing a good job in the employment of college graduates". Increasing the intensity of employment informatization is an effective means to promote employment. This article follows the software development lifecycle method to complete the research and design of the platform. The platform is divided into three major functional modules: personal job search, enterprise recruitment, and backend management. It mainly realizes the registration and login of two different types of user roles, the editing and delivery of resumes by job seekers online. The platform realizes intelligent recommendation and position retrieval based on job seeker search and collection records, as well as recruitment information management, enterprise management, and user review functions. The implementation of the platform meets the current job hunting needs of graduates, overcomes the time and space limitations of traditional offline job fairs, and uses recommendation algorithms to bring more accurate and effective recruitment information to job seekers. It can achieve unobstructed communication and exchange among the three parties and promote graduate employment.

Huili Chang, Tian Shi. Research and Design of a Job Search Service Platform Based on Recommendation Algorithm. The Frontiers of Society, Science and Technology (2023) Vol. 5, Issue 14: 78-83. https://doi.org/10.25236/FSST.2023.051414.

[1] Yu M, He W, Zhou X, Cui M, Wu K, Zhou W. (2022) Recommended Systematic review. Computer application, 42(6), 1898-1913. 

[2] Wang Y, Zhang J, Xu H. (2020)Combining user interest and improved Collaborative filtering recommendation algorithm. Mini microcomputer system, 41(8), 1665-1669. 

[3] Liu J, Li X. (2020)Progress in Personalized Recommendation System Technology. Computer Science, 47(7), 47-55. 

[4] Yu Z. (2020)Research on online job recommendation system based on deep learning. Dalian University of Technology, DOI:10. 26991/d. cnki. gdllu. 2020. 002293. 

[5] Hao L. (2019)Research and Design of Personalized Film Recommendation System. University of Electronic Science and Technology of China. 

[6] Cui L. (2022)Research on Collaborative filtering recommendation algorithm based on user characteristics and project type interests. Henan University of Economics and Law, DOI: 10. 27113/d. cnki. ghncc. 2022. 000460. 

[7] Hu Z. Du Y, Liu X. (2021) Design of a book automatic recommendation system based on user interest classification. Modern electronic technology, 44(6), 58-62. 

[8] Liu X. (2020)Algorithm for Employment Recommendation System in Vocational Colleges. Computer and Network, 46(23), 68-71. 

[9] Zhang M, Liu D. (2020)Research and Implementation of Music Recommendation System Based on Collaborative filtering Algorithm. Electronic World, 10, 63-64. 

[10] Zhao Y. (2020)Research on Job Recommendation System for College Graduates. Jiangsu University of Science and Technology, DOI:10. 27171/d. cnki. ghdcc. 2020. 000256. 

[11] Li Q. (2019)Research on personalized recommendation system based on Collaborative filtering. Anhui University of Science and Technology. 

[12] He R. (2019)Music recommendation system based on Convolutional neural network. Nanjing University of Posts and Telecommunications, DOI:10. 27251/d. cnki. gnjdc. 2019. 000205. 

[13] Zhao J. (2021)Research on problems and improvement strategies of Zhilian recruitment customer service satisfaction. Chongqing Technology and Business University, DOI:10. 27713/d. cnki. gcqgs. 2021. 000397. 

[14] Fuyuan Cheng. (2021)Talent Recruitment Management System for Small and MicroEnterprises Based on Springboot Framework. Advances in EducationalTechnology and Psychology, 5(2).