Sustainable Scientific and Technological Talents Recommendation Method Based on Recommendation Algorithm

Abstract

In today’s rapid development of global technology, the global demand for scientific and technological talents remains high. To provide a reliable talent referral channel, a talent recommendation model based on Bidirectional Encoder Representation from Transformers (BERT) and Bi-directional Long Short-Term Memory (BLSTM) was constructed. This model enables the matching of talented individuals with job opportunities. The results demonstrated that the accuracy and F1 value of BLSTM-BERT in the test set were 0.95 and 0.92, respectively. The precision rate, recall rate, F1-socre value and accuracy rate of BLSTM-CNN model were 0.96, 0.97, 0.96 and 0.97, respectively. The correct prediction rate of the talent recommendation model for the four types of talents was 1.0. It is evident that the talent recommendation model has a high accuracy in predicting talent categories and can precisely recommend necessary scientific and technological professionals for businesses.