Research on the Application of Deep Learning Technology in the Field of Human Resource Recruitment

Under the background of digital age, online recruitment has become a key way for enterprises to absorb human resources with diversified channels and dynamic data information. However, with the continuous explosive growth of job information, the disadvantages of traditional recruitment methods have become more and more prominent, and problems such as low recruitment efficiency and rising costs have followed. In this regard, this study focuses on the practical application requirements, focusing on the application feasibility of deep learning technology in the field of human resource recruitment, and designs and constructs an innovative human resource recommendation model to improve the processing efficiency of massive employment and recruitment information. The practical application shows that the model adopts a parallel structure as a whole, among which the factor decomposition machine model (FM) is excellent in dealing with sparse data, which can accurately capture the interaction between features and deeply explore the potential relationship between job seekers and positions. Residual Neural Network (ResNET) automatically learns the complex feature representation of data by virtue of deep network structure, and comprehensively considers the multidimensional factors of job seekers and the detailed requirements of positions. By organically combining FM and ResNET, the model can efficiently handle a large number of employment and recruitment information, and realize accurate matching and personalized recommendation of job seekers and positions.