Stochastic neural network based data analysis‐related talent recruitment optimization via CDN server

Abstract

The competition for talent is always intense, especially in the era of big data, the data analysis‐related talent recruitment plays an important role in promoting the development of enterprises. Regarding resume screening, it is considerably significant during the process of talent recruitment. Consider that Neural Network (NN) has the strong stability on the prediction result and is given a lot of attention, this paper uses Stochastic NN (SNN) to optimize the data analysis‐related talent recruitment. Meanwhile, SNN is used to train resume‐related dataset and make resume screening. In addition, in order to accelerate the training process and decrease the training complexity, this paper uses Locally Linear Embedding (LLE) to do dimension reduction in terms of the extracted data features. Furthermore, in order to guarantee the on‐line resume screening, this paper does resume screening under Content Delivery Network (CDN) scenario, where CDN has two functions, that is, data storage and hot resumes recommendation. The experiments are made based on the collected 7000 resumes, and the result show that this paper has good optimization effect on data analysis‐related talent recruitment.