孙莹, 章玉婷, 庄福振, 祝恒书, 何清, 熊辉. 基于集合效用边际贡献学习的可解释薪酬预测算法[J]. 计算机研究与发展.
 引用本文: 孙莹, 章玉婷, 庄福振, 祝恒书, 何清, 熊辉. 基于集合效用边际贡献学习的可解释薪酬预测算法[J]. 计算机研究与发展.
Sun Ying, Zhang Yuting, Zhuang Fuzhen, Zhu Hengshu, He Qing, Xiong Hui. Interpretable Salary Prediction Algorithm Based on Set Utility Marginal Contribution Learning[J]. Journal of Computer Research and Development.
 Citation: Sun Ying, Zhang Yuting, Zhuang Fuzhen, Zhu Hengshu, He Qing, Xiong Hui. Interpretable Salary Prediction Algorithm Based on Set Utility Marginal Contribution Learning[J]. Journal of Computer Research and Development.

## Interpretable Salary Prediction Algorithm Based on Set Utility Marginal Contribution Learning

• 摘要: 知识技能对薪酬影响作用视为一种多变量影响下高维元素集合的效用建模问题. 深度神经网络为解决复杂问题提供了新的机遇，但针对知识导向的细粒度薪酬预测问题，仍缺乏能够对复杂变量影响下的集合效用进行准确、可解释建模的神经网络结构. 为此，提出一种基于边际贡献的增量式集合效用网络 (marginal contribution-based incremental set utility network，MCISUN)来拟合元素加入时的效用增量，从而灵活且可解释地建模集合效用. 区别于以往基于池化层的排列不变性建模算法，MCISUN构建顺序敏感的中间结果，利用集合的排列不变性实现数据增强，有效提升模型数据效率及泛化性. 最后，大规模真实薪酬数据上的实验结果表明所提模型在基于技能的薪酬预测任务上比最先进的（state-of-the-art, SOTA）模型效果提升超过30%. 同时，定性实验证明模型能够为技能设置合理的贡献值且发现技能间的关联.

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.

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