Employment Recommendation for Education Talents Based on Big Data Precision Technology

Abstract

The cornerstone of future social education is the development of skills. This work enhances the big data algorithm and presents an effective association algorithm that may boost relevance in order to improve the accuracy of educational talent employment. Furthermore, this paper employs data mining association analysis technology to identify educational talent attributes that are disproportionately important in determining the problem of student employment, as well as to eliminate attributes that are unrelated to the problem, in order to achieve the goal of screening educational talent attributes. In addition, this paper combines an improved algorithm to build an education talent employment recommendation system, builds a system function structure based on the actual situation, and combines experimental research to verify the effect of the model built in this paper. From the experimental research, it can be seen that the education talent employment recommendation system based on big data precision technology constructed in this paper can play an important auxiliary role in the education talent employment recommendation.