Study And Implementation Of School-enterprise Talent Docking Platform Incorporating Web Data Mining

In the past few decades,Web data has been proliferating with the rapid development of the Internet.How to effectively mining hidden and valuable information from these innumerable Web data has become a research hotspot in the field of data mining.On the other hand,according to the survey,only 10% of college students found a job that was entirely suitable for their first employment.This reflects that current college students do not know whether the the knowledge coincide with works.And enterprises have long-term problems of accurately locating talents,high recruitment costs,long cycle times,poor results,difficulty in retaining talents,and difficult communication of talents.In view of the above problems,this paper proposes two Web data mining algorithms,and through the actual research,starting from the common pain points of current students and enterprises,and using the tea break culture as a medium,designed and built a system platform for talents docking between universities and enterprises..At the same time,two Web data mining algorithms are integrated to continuously improve the user experience.The main work of the thesis is:(1)A new graph-based post information document keyword extraction algorithm is proposed to mining the current popular keywords.The algorithm uses the keywords extracted by the traditional TextRank to construct the vertices,calculates the edge weights by time and click factors,and finally performs random walks to iteratively extract the keywords.(2)An improved AprioriAll algorithm is proposed to mining the user's preference path from the Web log.The algorithm reduces the number of scans of the database and only generates valid candidate sets by a priori clipping of the candidate set that does not satisfy the minimum support.Experiments show that compared with the original algorithm,the improved AprioriAll algorithm has lower space-time complexity and the mining performance is improved.(3)Using Spring Boot+Mybatis+Vue.js full stack technology,aschool-enterprise talent docking system platform is completely realized,and the above-mentioned algorithm is used to realize the post keyword extraction and user preference path mining module.Finally,the system is tested for function and performance.The results show that the Web data mining algorithm improves the efficiency of school-enterprise talent docking.