Research on the end-to-end job recommendation algorithm for resume texts
To increase the probability of candidates entering the initial recruitment interview.In this study,a dataset was built based on real on-site recruitment data of a large state-owned enterprise in Yunnan Province,and empirical research was carried out on job recommendation algorithms.Four kinds of machine learning models including random forest,xgboost model,GBDT model and LightGBM,as well as convolutional neural network model and BERT model in deep learning,were tested respectively by using the built dataset.Comparing the job recommendation results of six models,the BERT model performs the best in the job recommendation process,with a recommendation accuracy of 88.12%.This indicates that the BERT model can be used for job recommendation datasets and can achieve good recommendation results.In addition,the BERT model has relatively less processing of input data and is an end-to-end learning model that can be more conveniently applied to job recommendations.
job recommendationempirical researchmachine learningdeep learningbuilding a datasetBERT modelmodel comparisonend-to-end recommendation
万方数据
维普
