線上招聘服務的廣泛使用為就業市場帶來了豐富的資訊。數以萬計的職缺和履歷刊登於線上招聘平台中。然而人工招聘的方式往往花費許多成本，因此為招聘服務提供良好的職缺-履歷媒合至關重要。我們提出了一個新的模型來解決這個問題。首先，我們使用職缺和履歷的歷史記錄構建一個有號圖，並以目標職缺/履歷為中心提取子圖。之後，我們設計了兩個名為顯式意圖和隱性意圖的模塊來提取目標職缺/履歷的表示。在顯性意圖中，我們將從子圖中的鄰居學習目標職缺/履歷的重要需求/經驗。而在隱性意圖中，我們將從子圖中的歷史記錄學習目標職缺/履歷的歷史偏好。此外，我們利用圖卷積網絡增強了在上述模塊中子圖的節點表示。最後，通過總結雙重意圖來判斷職缺與履歷的媒合程度。我們也使用兩個線上招聘平台收集的數據進行實驗，驗證了我們的方法的表現優於最先進的方法。

The widespread use of online recruitment services bring a lot of information to the job market. Millions of jobs and resumes are available on online recruitment platforms. Although plenty of opportunities are provided, manual recruitment often costs a lot of time. Therefore, it is vital to provide good job-resume matching for recruitment services. However, recent algorithms are easily affected by the quantity and quality of available job-resume interactions. Therefore, we propose a novel model to tackle this problem. First, we construct a signed graph with the historical records and extract the subgraph based on the target job/resume. Afterward, we design two modules named the explicit intention and the implicit intention, respectively, to extract the representations of the target job/resume. In the explicit intention module, the important requirements/experiences of the target job/resume will be learned from the neighbors in the subgraph. In the implicit intention module, the historical preference of the target job/resume will be learned from the historical records. Moreover, we enhance the representations of nodes in the subgraph by the graph convolution network. Finally, the matching degree for person-job fitting is judged by summarizing dual intentions. Experiments on two real-world datasets collected from different online recruitment platforms show that the performance of our method is better than state-of-the-art methods.

[1] “Society for human resource management. 2016. 2016 human capital benchmarking report.”https://www.shrm.org/hr-today/trends-and-forecasting/research-and-surveys/documents/2016-human-capital-report.pdf.
[2] D. H. Lee and P. Brusilovsky, “Fighting information overflow with personalized comprehensive information access: A proactive job recommender,” in Third International Conference on Autonomic and Autonomous Systems (ICAS’07), pp. 21–21, IEEE, 2007.
[3] J. Malinowski, T. Keim, O. Wendt, and T. Weitzel, “Matching people and jobs: A bilateral recommendation approach,” in Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), vol. 6, pp. 137c–137c, IEEE, 2006.
[4] M. Diaby, E. Viennet, and T. Launay, “Toward the next generation of recruitment tools: an online social network-­based job recommender system,” in 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 821–828, IEEE, 2013.
[5] Y. Lu, S. El Helou, and D. Gillet, “A recommender system for job seeking and recruiting website,” in Proceedings of the 22nd International Conference on World Wide Web, pp. 963–966, 2013.
[6] Y. Zhang, C. Yang, and Z. Niu, “A research of job recommendation system based on collaborative filtering,” in 2014 Seventh International Symposium on Computational Intelligence and Design, vol. 1, pp. 533–538, IEEE, 2014.
[7] C. Zhu, H. Zhu, H. Xiong, C. Ma, F. Xie, P. Ding, and P. Li, “Person-­job fit: Adapting the right talent for the right job with joint representation learning,” ACM Transactions on Management Information Systems (TMIS), vol. 9, no. 3, pp. 1–17, 2018.
[8] C. Qin, H. Zhu, T. Xu, C. Zhu, L. Jiang, E. Chen, and H. Xiong, “Enhancing person-­job fit for talent recruitment: An ability-­aware neural network approach,” in The 41st international ACM SIGIR conference on research & development in information retrieval, pp. 25–34, 2018.
[9] Y. Luo, H. Zhang, Y. Wen, and X. Zhang, “Resumegan: An optimized deep representation learning framework for talent-­job fit via adversarial learning,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1101–1110, 2019.
[10] R. Le, W. Hu, Y. Song, T. Zhang, D. Zhao, and R. Yan, “Towards effective and interpretable person-job fitting,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1883–1892, 2019.
[11] S. Bian, W. X. Zhao, Y. Song, T. Zhang, and J.­R. Wen, “Domain adaptation for person-­job fit with transferable deep global match network,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP­IJCNLP), pp. 4812–4822, 2019.
[12] R. Yan, R. Le, Y. Song, T. Zhang, X. Zhang, and D. Zhao, “Interview choice reveals your preference on the market: To improve job-­resume matching through profiling memories,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 914–922, 2019.
[13] Y. Sun, F. Zhuang, H. Zhu, X. Song, Q. He, and H. Xiong, “The impact of person-­organization fit on talent management: A structure­aware convolutional neural network approach,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1625–1633, 2019.
[14] J. Jiang, S. Ye, W. Wang, J. Xu, and X. Luo, “Learning effective representations for person­-job fit by feature fusion,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2549–2556, 2020.
[15] S. Bian, X. Chen, W. X. Zhao, K. Zhou, Y. Hou, Y. Song, T. Zhang, and J.­R. Wen, “Learning to match jobs with resumes from sparse interaction data using multi­-view co-­teaching network,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 65–74,2020.
[16] R. Ramanath, H. Inan, G. Polatkan, B. Hu, Q. Guo, C. Ozcaglar, X. Wu, K. Kenthapadi, and S. C. Geyik, “Towards deep and representation learning for talent search at linkedin,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 2253–2261, 2018.
[17] T. N. Kipf and M. Welling, “Semi­-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
[18] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in European semantic web conference, pp. 593–607, Springer, 2018.
[19] T. Derr, Y. Ma, and J. Tang, “Signed graph convolutional networks,” in 2018 IEEE International Conference on Data Mining (ICDM), pp. 929–934, IEEE, 2018.
[20] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A convolutional neural network for modelling sentences,” arXiv preprint arXiv:1404.2188, 2014.
[21] Y. Kim, “Convolutional neural networks for sentence classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), (Doha, Qatar), pp. 1746– 1751, Association for Computational Linguistics, Oct. 2014.
[22] D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,” in Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 1422–1432, 2015.
[23] Q. Zhang, Y. Wang, Y. Gong, and X.­J. Huang, “Keyphrase extraction using deep recurrent neural networks on twitter,” in Proceedings of the 2016 conference on empirical methods in natural language processing, pp. 836–845, 2016.
[24] H. Dai, Z. Kozareva, B. Dai, A. Smola, and L. Song, “Learning steady­-states of iterative algorithms over graphs,” in International conference on machine learning, pp. 1106–1114, PMLR, 2018.
[25] Y. Seo, M. Defferrard, P. Vandergheynst, and X. Bresson, “Structured sequence modeling with graph convolutional recurrent networks,” in International Conference on Neural Information Processing, pp. 362–373, Springer, 2018.
[26] T. N. Kipf and M. Welling, “Variational graph auto-­encoders,” arXiv preprint arXiv:1611.07308, 2016.
[27] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” arXiv preprint arXiv:1310.4546, 2013.
[28] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.
[29] M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE transactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, 1997.