GRU-based capsule network with an improved loss for personnel performance prediction

Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human resource management (HRM). In this paper, to improve the performance, we propose a novel method for personnel performance prediction which helps decision-makers select high-potential talents. Specifically, for modeling the personnel performance, we first devise a GRU model to learn sequential information from personnel performance data without any expertise. Then, to better cluster the features, we exploit capsule network. Finally, to precisely make predictions, we further design one strategy, i.e., an improved loss function, and embed it into the capsule network. In addition, by introducing this strategy, our proposed model can well deal with the imbalanced data problem. Extensive experiments on real-world data clearly demonstrate the effectiveness of the proposed approach.

Automated summary

Predicting personnel future performance is crucial for maintaining core competitive advantages in human resource management. This paper proposes a novel method using GRU model and capsule network to predict personnel performance, along with an improved loss function. The experiments on real-world data demonstrate the effectiveness of the proposed approach in selecting high-potential talents and dealing with imbalanced data issues.