Abstract: Complex and diverse job information makes it difficult for many job seekers to find suitable job information. In order to improve the quality of human resource recommendation, this paper designs a targeted talent market recommendation model based on gradient lifting tree and hybrid convolution neural network. The flow distributed method is used to collect the information of job seekers and convert it into a unique hot code which can be used for algorithm analysis. The gradient lifting tree is used to extract the features of job seekers. After training, the hybrid convolution neural network can achieve targeted talent recommendation. Compared with the hybrid convolution neural network without gradient lifting tree and the convolution neural network with gradient lifting tree, the recall rate and F1 score of this model are improved by 9.78% and 10.1% respectively. This shows that the hybrid convolution neural network algorithm combined with gradient lifting tree can effectively improve the quality of human resource recommendation.

Key words: big data technology, gradient lifting tree, neural network, talent recommendation