Research On Personalized Employment Skills Recommendation System For College Students Based On Association Rules

With the rapid development of the Internet and online recruitment,the continuous increase of job information has brought difficulties to college students’ employment choices.At present,college students trained by colleges and universities lack a communication hub with recruitment enterprises,which recruit talents through qualitative job requirements and require college students to have a set of employment skills.However,college students study according to the courses carried out by colleges and universities,resulting in the lack of matching knowledge and skills combination,unable to meet the requirements of recruitment enterprises.Therefore,in order to improve the efficiency and success rate of college students’ job hunting and help them fully understand the changes in talent market demand,this dissertation studies and implements a personalized employment skills recommendation system for college students based on association rules according to recruitment information.Firstly,the traditional association rule recommendation algorithm is studied and compared,and the FP-growth algorithm is selected as the theoretical basis.Aiming at the problems of low operation efficiency and unscientific selection of recommended parameters in traditional and Spark-based FP-growth algorithms,this dissertation improves the algorithm from three aspects: grouping strategy,item header table structure and FP-tree structure,and recommended parameter selection.The improved algorithm is run on the Spark cluster,and the improvement of the operation efficiency of the improved algorithm is verified through multiple sets of experiments.The experimental results show that the improved FP-growth algorithm based on Spark has good performance in terms of data scale,number of cluster nodes,and selection of recommended parameters.Secondly,based on SSM framework,Vue framework design and implementation of a college students personalized employment skills recommendation system,its front and back interaction using Java Script language and Ajax.The system includes data acquisition and preprocessing,data keyword extraction,personalized recommendation and user information management.Data collection uses Python crawler technology to collect job recruitment information from recruitment websites;data keyword extraction uses information extraction technology to obtain recruitment information keywords;personalized recommendation function is the core function of the system,which includes the following two sub-functions:(1)Employment skills recommendation for college students.The improved FP-growth algorithm based on Spark is used to dig out the general employment skill group corresponding to the job from the massive job recruitment information on the Internet and make recommendations.(2)Personalized employment skills recommendation for college students.This paper studies a personalized recommendation strategy which combines the interest value of college students,the mastery skill group of college students and the general employment skill group of positions,and provides services for college students according to the personalized recommendation strategy.College students personalized employment skills,the application of recommender system can help college students are more aware of the interested in general employability skills required for the position as well as skills,the relationship between could be more targeted to study relevant theoretical basis,for college students when entering the workplace in their cognitive and employment intention to provide a reliable reference.