A Deep‐Learning‐Inspired Person‐Job Matching Model Based on Sentence Vectors and Subject‐Term Graphs
Xiaowei Wang, Zhenhong Jiang, Lingxi Peng
Abstract
In this study, an end‐to‐end person‐to‐job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low‐dimensional space is described. Then, it is explained in the Bidirectional Encoder Representations from Transformers (BERT) pretraining language model, which is introduced as the encoding model for textual information. The structure of the person‐post matching model is explained in terms of the attention mechanism and its computational layers. Finally, the experiments based on the person‐post matching model are compared with a variety of person‐post matching methods in the actual recruitment dataset, and the experimental results are analyzed.