{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:46:28Z","timestamp":1781678788961,"version":"3.54.5"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T00:00:00Z","timestamp":1537920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>Person-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 talent competencies as well as the job\u2019s talent requirements. To this end, in this article, we propose a novel end-to-end data-driven model based on a Convolutional Neural Network (CNN), namely, the Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network that can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.<\/jats:p>","DOI":"10.1145\/3234465","type":"journal-article","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T16:25:19Z","timestamp":1537979119000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":136,"title":["Person-Job Fit"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-482X","authenticated-orcid":false,"given":"Chen","family":"Zhu","sequence":"first","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Business Intelligence Lab, Baidu Research, Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Ma","sequence":"additional","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Xie","sequence":"additional","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengliang","family":"Ding","sequence":"additional","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Li","sequence":"additional","affiliation":[{"name":"Talent Intelligence Center, Baidu, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,9,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487704"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1129"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2492517.2500266"},{"key":"e_1_2_1_5_1","volume-title":"Accessed","author":"Factbook Talent Acquisition","year":"2016"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of International Conference on Learning Representations.","author":"Hermann Karl Moritz","year":"2014"},{"key":"e_1_2_1_7_1","volume-title":"Making Vocational Choices: A Theory of Careers","author":"Holland John L."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSE.2013.6554164"},{"key":"e_1_2_1_9_1","volume-title":"International Conference on Machine Learning. 448--456","author":"Ioffe Sergey","year":"2015"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1062"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_2_1_12_1","volume-title":"Adam: A method for stochastic optimization. arXiv Preprint arXiv:1412.6980","author":"Kingma Diederik","year":"2014"},{"key":"e_1_2_1_13_1","volume-title":"Learning multilingual word representations using a bag-of-words autoencoder. arXiv Preprint arXiv:1401.1803","author":"Lauly Stanislas","year":"2014"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939721"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of AAAI. 1417--1423","author":"Lin Hao","year":"2017"},{"key":"e_1_2_1_16_1","volume-title":"Accessed","author":"Wikipedia In","year":"2017"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487788.2488092"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/HICSS.2006.266"},{"key":"e_1_2_1_19_1","volume-title":"Efficient estimation of word representations in vector space. arXiv Preprint arXiv:1301.3781","author":"Mikolov Tomas","year":"2013"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/1699571.1699627"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 27th International Conference on Machine Learning (ICML\u201910)","author":"Nair Vinod"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2043994"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210025"},{"key":"e_1_2_1_24_1","volume-title":"Organizational Behavior","author":"Robbins Stephen P.","edition":"14"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/492"},{"key":"e_1_2_1_26_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014"},{"key":"e_1_2_1_27_1","unstructured":"Oriol Vinyals \u0141ukasz Kaiser Terry Koo Slav Petrov Ilya Sutskever and Geoffrey Hinton. 2015. 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