Position information visualization analysis and personalized recommendation based on ant colony - Xin, Ling; Zhou, Bin; Liu, Pan (EAI Endorsed Transactions on Scalable Information Systems, 2024)
With the rapid development of network technology, online recruitment and job hunting have become an important way of job hunting at present, but job seekers spend a lot of time looking for suitable positions in the face of massive job information. Traditional artificial selection of job information is difficult to solve the problem of job seekers finding suitable positions quickly and accurately. This article is based on ant colony algorithm for visual analysis and personalized recommendation of job information. Through visual analysis of massive job information on the network, personalized recommendations are made based on job seekers’ professional, skill, behavior, and other information. A visual analysis and personalized recommendation system for job information is established, and recommendation accuracy, efficiency, and recall rate are evaluated and analyzed using recommendation theory, realize comprehensive evaluation of information visualization analysis and personalized recommendation quality of position information based on ant colony algorithm. Compared with artificial selection of position information, it is fast and highly matched.