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Manage. Inf. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Ability assessment is a critical task in talent recruitment that aims at identifying the most suitable job candidates by evaluating the alignment of their skills with job requirements. Indeed, traditional ability assessment involves multiple staffing processes with various forms of evaluation methods, such as written tests and face-to-face interviews, which usually result in fragmented, noisy, and inconsistent conclusions. Therefore, a long-standing challenge in talent recruitment is how to comprehensively evaluate candidates by integrating multi-source heterogeneous assessment results. To this end, in this article, we propose a holistic framework, JCD-TR (\n            <jats:bold>J<\/jats:bold>\n            oint\n            <jats:bold>C<\/jats:bold>\n            ognitive\n            <jats:bold>D<\/jats:bold>\n            iagnosis for\n            <jats:bold>T<\/jats:bold>\n            alent\n            <jats:bold>R<\/jats:bold>\n            ecruitment), for enhancing the performance of ability assessment in talent recruitment by jointly modeling the multi-source heterogeneous assessment results. Specifically, we first construct a skill graph based on the co-occurrence relations of skills in multi-source recruitment data. Along this line, we can learn the skill representations that maintain both the semantic and structural information with graph embedding. Then, we design a multi-source candidate ability profiling module with the guidance of item response theory in psychometrics and the neural topic model. As a result, the candidates\u2019 ability profiles can be explored from their resumes, written tests, and interview assessment data, respectively. Furthermore, we propose a joint cognitive diagnosis module by integrating those multi-view ability profiles and skill representations to assess the candidates\u2019 skill proficiency state. Extensive experiments on a real-world dataset demonstrate the effectiveness of our JCD-TR.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3714414","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T11:39:43Z","timestamp":1737718783000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Joint Ability Assessment for Talent Recruitment: A Neural Cognitive Diagnosis Approach"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-6855","authenticated-orcid":false,"given":"Haiping","family":"Ma","sequence":"first","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3497-0633","authenticated-orcid":false,"given":"Manwei","family":"Li","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5354-8630","authenticated-orcid":false,"given":"Chuan","family":"Qin","sequence":"additional","affiliation":[{"name":"Computer Network Information Center Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3947-4153","authenticated-orcid":false,"given":"Dazhong","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai China"},{"name":"University of Science and Technology of China, Hefei China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-000X","authenticated-orcid":false,"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology - Guangzhou Campus, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Anumeha Agrawal Rosa Anil George Selvan Sunitha Ravi et\u00a0al. 2020. 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