Interpretable Salary Prediction Algorithm Based on Set Utility Marginal Contribution Learning

Sun Ying; Zhang Yuting; Zhuang Fuzhen; Zhu Hengshu; He Qing; Xiong Hui

doi:10.7544/issn1000-1239.202330133

Journal of Computer Research and Development > 2024 > 61(5): 1276-1289. > DOI: 10.7544/issn1000-1239.202330133

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, 2024, 61(5): 1276-1289. DOI: 10.7544/issn1000-1239.202330133

Citation:

PDF (2060 KB)

Interpretable Salary Prediction Algorithm Based on Set Utility Marginal Contribution Learning

Sun Ying^1,,
Zhang Yuting^{2, 6},
Zhuang Fuzhen^3, ,,
Zhu Hengshu^4, ,,
He Qing^{5, 6},
Xiong Hui¹

1.
Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458
2.
Special Technology Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190
3.
Institute of Artificial Intelligence, Beihang University, Beijing 100191
4.
Career Science Lab, BOSS Zhipin, Beijing 100028
5.
CAS Key Laboratory of Intelligent Information Processing (Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190
6.
University of Chinese Academy of Sciences, Beijing 101408

Funds: This work was supported by the National Natural Science Foundation of China (62176014, 61836013), the City-University Joint Funding Project of Guangzhou Science and Technology Plan (2023A03J0141), and the Fundamental Research Funds for the Central Universities.

More Information

Author Bio:
Sun Ying: born in 1994. PhD, assistant professor, PhD supervisor. Member of CCF. Her main research interests include machine learning and data mining

Zhang Yuting: born in 1998. Master candidate. Her main research interests include machine learning and data mining

Zhuang Fuzhen: born in 1983. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include machine learning and data mining

Zhu Hengshu: born in 1986. PhD, professor of engineering. Senior member of CCF. His main research interests include machine learning and data mining

He Qing: born in 1965. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include machine learning and data mining

Xiong Hui: born in 1972. PhD, professor, PhD supervisor. Senior member of CCF. His main research interest includes data and knowledge engineering
Received Date: March 09, 2023
Revised Date: July 25, 2023
Available Online: March 06, 2024

Graphical Abstract

Abstract

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.
- set utility modeling,
- marginal contribution,
- salary prediction,
- neural network,
- interpretability

FullText(HTML)

References (43)

References

[1]	Hamlen K R, Hamlen W A. Faculty salary as a predictor of student outgoing salaries from MBA programs[J]. Journal of Education for Business, 2016, 91(1): 38−44 doi: 10.1080/08832323.2015.1110552
[2]	Khongchai P, Songmuang P. Implement of salary prediction system to improve student motivation using data mining technique[C/OL]//Proc of the 11th Int Conf on Knowledge, Information and Creativity Support Systems (KICSS). Piscataway, NJ: IEEE, 2016[2023-06-25].https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7951419
[3]	Khongchai P, Songmuang P. Random forest for salary prediction system to improve students’ motivation[C]//Proc of the 12th Int Conf on Signal-Image Technology and Internet-Based Systems (SITIS). Piscataway, NJ: IEEE, 2016: 637−642
[4]	Bansal U, Narang A, Sachdeva A, et al. Empirical analysis of regression techniques by house price and salary prediction[C/OL]// Proc of the IOP Conf Series: Materials Science and Engineering. 2021[2023-06-25].https://iopscience.iop.org/article/10.1088/1757-899X/1022/1/012110/pdf
[5]	马新宇,范意兴,郭嘉丰,等. 关于短文本匹配的泛化性和迁移性的研究分析[J]. 计算机研究与发展,2022,59(1):118−126 Ma Xinyu, Fan Yixing, Guo Jiafeng, et al. An empirical investigaion of generalization and transfer in short text matching[J]. Journal of Computer Research and Development, 2022, 59(1): 118−126 (in Chinese)
[6]	潘博,张青川,于重重,等. Doc2vec 在薪水预测中的应用研究[J]. 计算机应用研究,2018,35(1):155−157 doi: 10.3969/j.issn.1001-3695.2018.01.032 Pan Bo, Zhang Qingchuan, Yu Chongchong, et al. Research on the application of Doc2vec in salary forecast[J]. Application Research of Computers, 2018, 35(1): 155−157 (in Chinese) doi: 10.3969/j.issn.1001-3695.2018.01.032
[7]	More A, Naik A, Rathod S. Predict-nation skills based salary prediction for freshers[C/OL]//Proc of the 4th Int Conf on Advances in Science & Technology (ICAST2021). Berlin: Springer, 2021[2023-06-25].https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3866758
[8]	Martín I, Mariello A, Battiti R, et al. Salary prediction in the IT job market with few high-dimensional samples: A spanish case study[J]. International Journal of Computational Intelligence Systems, 2018, 11(1): 1192−1209 doi: 10.2991/ijcis.11.1.90
[9]	Sun Ying, Zhuang Fuzhen, Zhu Hengshu, et al. Market-oriented job skill valuation with cooperative composition neural network[J]. Nature Communications, 2021, 12(1): 1−12 doi: 10.1038/s41467-020-20314-w
[10]	Zaheer M, Kottur S, Ravanbakhsh S, et al. Deep sets[C]//Advances in Neural Information Processing Systems 30. Cambridge, MA: MIT, 2017[2023-06-25].https://proceedings.neurips.cc/paper/2017/file/f22e4747da1aa27e363d86d40ff442fe-Paper.pdf
[11]	Vinyals O, Bengio S, Kudlur M. Order matters: Sequence to sequence for sets[J]. arXiv preprint, arXiv: 1511.06391, 2015
[12]	Lee J, Lee Y, Kim J, et al. Set Transformer: A framework for attention-based permutation-invariant neural networks[C]// Proc of the 36th Int Conf on Machine Learning. New York: ACM, 2019: 3744−3753
[13]	Zhang Yan, Hare J, Prügel-Bennett A. FSPool: Learning set representations with featurewise sort pooling[C/OL]//Proc of the 8th Int Conf on Learning Representations. 2020[2023-06-25].https://openreview.net/forum?id=HJgBA2VYwH
[14]	Murphy R L, Srinivasan B, Rao V, et al. Janossy Pooling: Learning deep permutation-invariant functions for variable-size inputs[C/OL]//Proc of the 8th Int Conf on Learning Representations. 2020[2023-06-25].https://openreview.net/forum?id=BJluy2RcFm
[15]	Yang Bo, Wang Sen, Markham A, et al. Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction[J]. International Journal of Computer Vision, 2020, 128(1): 53−73
[16]	Saito Y, Nakamura T, Hachiya H, et al. Exchangeable deep neural networks for set-to-set matching and learning[C]//Proc of the 17th European Conf on Computer Vision. Berlin: Springer, 2020: 626−646
[17]	Zhang Yan, Hare J, Prügel-Bennett A. Learning representations of sets through optimized permutations[C/OL]//Proc of the 7th Int Conf on Learning Representations. 2019[2023-06-25].https://openreview.net/forum?id=HJMCcjAcYX
[18]	Blankmeyer E, LeSage J P, Stutzman J R, et al. Peer ‐ group dependence in salary benchmarking: A statistical model[J]. Managerial and Decision Economics, 2011, 32(2): 91−104
[19]	Kenthapadi K, Ambler S, Zhang Liang, et al. Bringing salary transparency to the world: Computing robust compensation insights via LinkedIn Salary[C]//Proc of the 26th ACM on Conf on Information and Knowledge Management. New York: ACM, 2017: 447−455
[20]	张浩宇. 基于文本相似度与协同过滤的岗位薪资预测[D]. 广州:中南财经政法大学,2018 Zhang Haoyu. Job salary prediction based on text similarity and collaborative filtering[D]. Guangzhou: Zhongnan University of Economics and Law, 2018 (in Chinese)
[21]	Meng Qingxin, Xiao Keli, Shen Dazhong, et al. Fine-grained job salary benchmarking with a nonparametric Dirichlet process–based latent factor model[J]. INFORMS Journal on Computing, 2022, 34(5): 2443−2463 doi: 10.1287/ijoc.2022.1182
[22]	Meng Qingxin, Zhu Hengshu, Xiao Keli, et al. Intelligent salary benchmarking for talent recruitment: A holistic matrix factorization approach[C]//Proc of the 2018 IEEE Int Conf on Data Mining (ICDM). Piscataway, NJ: IEEE, 2018: 337−346
[23]	Wang Zhongsheng, Sugaya S, Nguyen D P T. Salary prediction using bidirectional-GRU-CNN model[C/OL]//Proc of the 25th Annual Meeting of the Association for Natural Language Processing. 2019[2023-06-25].https://www.anlp.jp/proceedings/annual_meeting/2019/pdf_dir/F3-1.pdf
[24]	Guo Huifeng, Tang Ruiming, Ye Yunming, et al. DeepFM: A factorization-machine based neural network for CTR prediction [C]//Proc of the 26th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2017: 1725−1731
[25]	Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735−1780 doi: 10.1162/neco.1997.9.8.1735
[26]	Sun Ying, Zhuang Fuzhen, Zhu Hengshu, et al. Job posting data[CP/OL]. 2021[2023-06-25].https://figshare.com/articles/dataset/Job_Posting_Data/14060498/
[27]	Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C/OL]//Proc of the 30th Int Conf on Artificial Intelligence and Statistics. New York: ACM, 2010[2023-06-25]. http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
[28]	Kingma D P, Ba J. Adam: A method for stochastic optimization[C/OL]//Proc of the 3rd Int Conf on Learning Representations (Poster). 2015[2023-06-25].https://iclr.cc/archive/www/doku.php%3Fid=iclr2015:accepted-main.html
[29]	Xu Bing, Wang Naiyan, Chen Tianqi, et al. Empirical evaluation of rectified activations in convolutional network[J]. arXiv preprint, arXiv: 1505.00853, 2015
[30]	Noble W S. What is a support vector machine?[J]. Nature Biotechnology, 2006, 24(12): 1565−1567 doi: 10.1038/nbt1206-1565
[31]	Montgomery D C, Peck E A, Vining G G. Introduction to Linear Regression Analysis[M]. Hoboken: John Wiley & Sons, 2021
[32]	Mason L, Baxter J, Bartlett P, et al. Boosting algorithms as gradient descent[C/OL]//Advances in Neural Information Processing Systems 12. Cambridge, MA: MIT, 1999[2023-06-25].https://proceedings.neurips.cc/paper/1999/file/96a93ba89a5b5c6c226e49b88973f46e-Paper.pdf
[33]	Gardner M W, Dorling S R. Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences[J]. Atmospheric Environment, 1998, 32(14/15): 2627−2636
[34]	Chen Yahui. Convolutional neural network for sentence classification[D]. Waterloo: University of Waterloo, 2015
[35]	Zhang Xiang, Zhao Junbo, LeCun Y. Character-level convolutional networks for text classification[C/OL]//Advances in Neural Information Processing Systems 28. Cambridge, MA: MIT, 2015[2023-06-25]. https://proceedings.neurips.cc/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf
[36]	Yang Zichao, Yang Diyi, Dyer C, et al. Hierarchical attention networks for document classification[C]//Proc of the 15th North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: ACL, 2016: 1480−1489
[37]	Dai Zihang, Yang Zhilin, Yang Yiming, et al. Transformer-Xl: Attentive language models beyond a fixed-length context[C/OL]//Proc of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2019[2023-06-25].https://arxiv.org/pdf/1901.02860.pdf%3Ffbclid%3DIwAR3nwzQA7VyD36J6u8nEOatG0CeW4FwEU_upvvrgXSES1f0Kd-
[38]	Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proc of the 17th Annual Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: ACL, 2019: 4171−4186
[39]	Liu Yinhan, Ott M, Goyal N, et al. RoBERTa: A robustly optimized BERT pretraining approach[J]. arXiv preprint, arXiv: 1907.11692, 2019
[40]	Yang Zhilin, Dai Zihang, Yang Yiming, et al. XLNet: Generalized autoregressive pretraining for language understanding[C/OL]//Advances in Neural Information Processing Systems 32. Cambridge, MA: MIT, 2019[2023-06-25].https://proceedings.neurips.cc/paper/2019/file/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf
[41]	Zhang Yan, Hare J, Prugel-Bennett A. Deep set prediction networks[C/OL]//Advances in Neural Information Processing Systems 32. Cambridge, MA: MIT, 2019 [2024-03-29]. https://proceedings.neurips.cc/paper_files/paper/2019/file/6e79ed05baec2754e25b4eac73a332d2-Paper.pdf
[42]	Botchkarev A. A new typology design of performance metrics to measure errors in machine learning regression algorithms[J]. Interdisciplinary Journal of Information, Knowledge, and Management, 2019, 14: 45−79
[43]	Blum A, Kalai A, Langford J. Beating the hold-out: Bounds for k-fold and progressive cross-validation[C]//Proc of the 12th Annual Conf on Computational Learning Theory. New York: ACM, 1999: 203−208

[1]	Liu Chunhong, Li Weili, Jiao Jie, Wang Jingxiong, Zhang Junna. An Interpretable Cloud Platform Task Termination State Prediction Method[J]. Journal of Computer Research and Development, 2024, 61(3): 716-727. DOI: 10.7544/issn1000-1239.202220796
[2]	Wu Huanhuan, Xie Ruilin, Qiao Yuanxin, Chen Xiang, Cui Zhanqi. Optimizing Deep Neural Network Based on Interpretability Analysis[J]. Journal of Computer Research and Development, 2024, 61(1): 209-220. DOI: 10.7544/issn1000-1239.202220803
[3]	Liu Maofu, Bi Jianqi, Zhou Bingying, Hu Huijun. Interpretable Image Caption Generation Based on Dependency Syntax[J]. Journal of Computer Research and Development, 2023, 60(9): 2115-2126. DOI: 10.7544/issn1000-1239.202220432
[4]	Zhu Haiping, Zhao Chengcheng, Liu Qidong, Zheng Qinghua, Zeng Jiangwei, Tian Feng, Chen Yan. Reciprocal-Constrained Interpretable Job Recommendation[J]. Journal of Computer Research and Development, 2021, 58(12): 2660-2672. DOI: 10.7544/issn1000-1239.2021.20211008
[5]	Sun Jianwen, Zhou Jianpeng, Liu Sannüya, He Feijuan, Tang Yun. Hierarchical Attention Network Based Interpretable Knowledge Tracing[J]. Journal of Computer Research and Development, 2021, 58(12): 2630-2644. DOI: 10.7544/issn1000-1239.2021.20210997
[6]	Zhang Bing, Wen Zheng, Wei Xiaoyu, Ren Jiadong. InterDroid: An Interpretable Android Malware Detection Method for Conceptual Drift[J]. Journal of Computer Research and Development, 2021, 58(11): 2456-2474. DOI: 10.7544/issn1000-1239.2021.20210560
[7]	Chen Kerui, Meng Xiaofeng. Interpretation and Understanding in Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(9): 1971-1986. DOI: 10.7544/issn1000-1239.2020.20190456
[8]	Cheng Keyang, Wang Ning, Shi Wenxi, Zhan Yongzhao. Research Advances in the Interpretability of Deep Learning[J]. Journal of Computer Research and Development, 2020, 57(6): 1208-1217. DOI: 10.7544/issn1000-1239.2020.20190485
[9]	Ji Shouling, Li Jinfeng, Du Tianyu, Li Bo. Survey on Techniques, Applications and Security of Machine Learning Interpretability[J]. Journal of Computer Research and Development, 2019, 56(10): 2071-2096. DOI: 10.7544/issn1000-1239.2019.20190540
[10]	Pan Xiaoyan, Lou Zhengzheng, Ji Bo, Ye Yangdong. Interpretable Clustering with Multi-View Generative Model[J]. Journal of Computer Research and Development, 2017, 54(8): 1713-1723. DOI: 10.7544/issn1000-1239.2017.20170175