ABSTRACT
Person-job fit is to match candidates and job posts on online recruitment platforms using machine learning algorithms. The effectiveness of matching algorithms heavily depends on the learned representations for the candidates and job posts. In this paper, we propose to learn comprehensive and effective representations of the candidates and job posts via feature fusion. First, in addition to applying deep learning models for processing the free text in resumes and job posts, which is adopted by existing methods, we extract semantic entities from the whole resume (and job post) and then learn features for them. By fusing the features from the free text and the entities, we get a comprehensive representation for the information explicitly stated in the resume and job post. Second, however, some information of a candidate or a job may not be explicitly captured in the resume or job post. Nonetheless, the historical applications including accepted and rejected cases can reveal some implicit intentions of the candidates or recruiters. Therefore, we propose to learn the representations of implicit intentions by processing the historical applications using LSTM. Last, by fusing the representations for the explicit and implicit intentions, we get a more comprehensive and effective representation for person-job fit. Experiments over 10 months real data show that our solution outperforms existing methods with a large margin. Ablation studies confirm the contribution of each component of the fused representation. The extracted semantic entities help interpret the matching results in the case study.
Supplemental Material
- 2016. RecSys Challenge '16: Proceedings of the Recommender Systems Challenge (Boston, Massachusetts, USA). Association for Computing Machinery, New York, NY, USA.Google Scholar
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In ICLR,, Yoshua Bengio and Yann LeCun (Eds.).Google Scholar
- D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba. 2017. Network Dissection: Quantifying Interpretability of Deep Visual Representations. In CVPR. 3319--3327.Google Scholar
- Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, and Ji-Rong Wen. 2019. Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network. In EMNLP-IJCNLP. Association for Computational Linguistics, Hong Kong, China, 4810--4820. https://doi.org/10.18653/v1/D19-1487Google Scholar
- Yu Cheng, Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok N. Choudhary, and Songtao Guo. 2013. JobMiner: a real-time system for mining job-related patterns from social media. In SIGKDD. ACM, 1450--1453. https://doi.org/10.1145/2487575.2487704Google ScholarDigital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. Association for Computational Linguistics, 4171--4186. https://doi.org/10.18653/v1/n19--1423Google Scholar
- Mamadou Diaby, Emmanuel Viennet, and Tristan Launay. 2013a. Toward the next generation of recruitment tools: an online social network-based job recommender system. In ASONAM,, Jon G. Rokne and Christos Faloutsos (Eds.). ACM, 821--828. https://doi.org/10.1145/2492517.2500266Google ScholarDigital Library
- Mamadou Diaby, Emmanuel Viennet, and Tristan Launay. 2013b. Toward the next Generation of Recruitment Tools: An Online Social Network-Based Job Recommender System. In ASONAM. Association for Computing Machinery, New York, NY, USA, 821--828. https://doi.org/10.1145/2492517.2500266Google ScholarDigital Library
- Yoav Goldberg. 2015. A Primer on Neural Network Models for Natural Language Processing. CoRR, Vol. abs/1510.00726 (2015). arxiv: 1510.00726 http://arxiv.org/abs/1510.00726Google Scholar
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In IJCAI,, Carles Sierra (Ed.). ijcai.org, 1725--1731. https://doi.org/10.24963/ijcai.2017/239Google Scholar
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. (2020). arxiv: cs.IR/2002.02126Google Scholar
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry P. Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM,, Qi He, Arun Iyengar, Wolfgang Nejdl, Jian Pei, and Rajeev Rastogi (Eds.). ACM, 2333--2338. https://doi.org/10.1145/2505515.2505665Google ScholarDigital Library
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In NIPS. 3146--3154.Google Scholar
- Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. Association for Computational Linguistics, Doha, Qatar, 1746--1751. https://doi.org/10.3115/v1/D14-1181Google Scholar
- DP Kingma and Ba J Adam. 2017. A method for stochastic optimization. cornell university library. arXiv preprint arXiv:1412.6980 (2017).Google Scholar
- Pang Wei Koh and Percy Liang. 2017. Understanding Black-box Predictions via Influence Functions. In ICML (Proceedings of Machine Learning Research),, Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 1885--1894. http://proceedings.mlr.press/v70/koh17a.htmlGoogle Scholar
- Ran Le, Wenpeng Hu, Yang Song, Tao Zhang, Dongyan Zhao, and Rui Yan. 2019. Towards Effective and Interpretable Person-Job Fitting. In CIKM,, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 1883--1892. https://doi.org/10.1145/3357384.3357949Google ScholarDigital Library
- LinkedIn. LinkedIn Workforce Report|United States. https://economicgraph.linkedin.com/resources/linkedin-workforce-report-march-2020Google Scholar
- Zachary Chase Lipton. 2016. The Mythos of Model Interpretability. CoRR, Vol. abs/1606.03490 (2016). arxiv: 1606.03490 http://arxiv.org/abs/1606.03490Google Scholar
- Yao Lu, Sandy El Helou, and Denis Gillet. 2013. A recommender system for job seeking and recruiting website. In WWW. International World Wide Web Conferences Steering Committee / ACM, 963--966. https://doi.org/10.1145/2487788.2488092Google ScholarDigital Library
- J. Malinowski, T. Keim, O. Wendt, and T. Weitzel. 2006. Matching People and Jobs: A Bilateral Recommendation Approach. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), Vol. 6. 137c--137c.Google Scholar
- Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match using Local and Distributed Representations of Text for Web Search. In WWW,, Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 1291--1299. https://doi.org/10.1145/3038912.3052579Google ScholarDigital Library
- Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, and Hui Xiong. 2018. Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach. In SIGIR. ACM, 25--34. https://doi.org/10.1145/3209978.3210025Google ScholarDigital Library
- Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, and Sahin Cem Geyik. 2018. Towards Deep and Representation Learning for Talent Search at LinkedIn. In CIKM. ACM, 2253--2261. https://doi.org/10.1145/3269206.3272030Google ScholarDigital Library
- Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In ICCV. IEEE Computer Society, 618--626. https://doi.org/10.1109/ICCV.2017.74Google Scholar
- Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. 2016. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. In SIGKDD. ACM, 255--262. https://doi.org/10.1145/2939672.2939704Google ScholarDigital Library
- Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In NIPS. 3104--3112.Google ScholarDigital Library
- Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, and Jimmy Lin. 2019. Distilling Task-Specific Knowledge from BERT into Simple Neural Networks. CoRR, Vol. abs/1903.12136 (2019). arxiv: 1903.12136Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008.Google Scholar
- Jian Wang, Yi Zhang, Christian Posse, and Anmol Bhasin. 2013. Is it time for a career switch?. In WWW. International World Wide Web Conferences Steering Committee / ACM, 1377--1388. https://doi.org/10.1145/2488388.2488509Google ScholarDigital Library
- Huang Xu, Zhiwen Yu, Jingyuan Yang, Hui Xiong, and Hengshu Zhu. 2019. Dynamic Talent Flow Analysis with Deep Sequence Prediction Modeling. IEEE Trans. Knowl. Data Eng., Vol. 31, 10 (2019), 1926--1939. https://doi.org/10.1109/TKDE.2018.2873341Google ScholarCross Ref
- Rui Yan, Ran Le, Yang Song, Tao Zhang, Xiangliang Zhang, and Dongyan Zhao. 2019. Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories. In SIGKDD. ACM, 914--922. https://doi.org/10.1145/3292500.3330963Google ScholarDigital Library
- Yingya Zhang, Cheng Yang, and Zhixiang Niu. 2015. A Research of Job Recommendation System Based on Collaborative Filtering. ISCID, Vol. 1 (03 2015), 533--538.Google Scholar
- Chen Zhu, Hengshu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, and Pan Li. 2018. Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning. ACM Transactions on Management Information Systems, Vol. 9 (09 2018), 1--17. https://doi.org/10.1145/3234465Google ScholarDigital Library
Recommendations
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
Research Commentary and Regular PapersPerson-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks quantitative ways of measuring ...
Towards Effective and Interpretable Person-Job Fitting
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge ManagementThe diversity of job requirements and the complexity of job seekers' abilities put forward higher requirements for the accuracy and interpretability of Person-Job Fit system. Interpretable Person-Job Fit system can show reasons for giving ...
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalThe wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right ...
Comments