Abstract: Accurately quantifying the relationship between skills and salary is essential to improve reasonable job salary setting and promote talent attraction and retention. However, the relationship between skills and salary is complex because it involves modeling set utility in a high-dimensional space with massive possible elements. Deep neural networks offer a new solution for complex fitting problems. However, for skill-based fine-grained salary prediction, there still lacks interpretable neural networks that can effectively model set utility under the influence of complex variables. To address this issue, we propose a marginal contribution-based incremental set utility network (MCISUN). MCISUN models the marginal contribution of elements when they are added to the set. In this way, the set utility can be naturally obtained in a flexible and interpretable way. In particular, rather than relying on pooling structures to ensure permutation invariance, MCISUN constructs order-sensitive intermediate results through recurrent attention neural networks and takes advantage of the sets’ permutation invariance property to achieve data augmentation, thus improving the model’s robustness. We conduct extensive experiments on a real-world large-scale salary dataset. The experimental results show that MCISUN outperforms state-of-the-art models by 30% for skill-based salary prediction. Qualitative experiments show that our model can recognize reasonable skill contribution values and capture the relationship between skills.