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Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in this field mainly focus on a single fixed scale, investigating either individual trajectories at the micro-level or crowd flows among market entities at the macro-level. Consequently, the intrinsic cross-scale interactions between talents and the labor market are largely overlooked. To bridge this gap, we propose\n            <jats:bold>UniTRep<\/jats:bold>\n            , a novel unified representation learning framework for cross-scale career mobility analysis. Specifically, we first introduce a trajectory hypergraph structure to organize the career mobility patterns in a low-information-loss manner, where market entities and talent trajectories are represented as nodes and hyperedges, respectively. Then, for learning the\n            <jats:italic>market-aware talent representations<\/jats:italic>\n            , we attentively propagate the node information to the hyperedges and incorporate the market contextual features into the process of individual trajectory modeling. For learning the\n            <jats:italic>trajectory-enhanced market representations<\/jats:italic>\n            , we aggregate the message from hyperedges associated with a specific node to integrate the fine-grained semantics of trajectories into labor market modeling. Moreover, we design two auxiliary tasks to optimize both intra-scale and cross-scale learning with a self-supervised strategy. Extensive experiments on a real-world dataset clearly validate that UniTRep can significantly outperform state-of-the-art baselines for various tasks.\n          <\/jats:p>","DOI":"10.1145\/3651158","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T12:06:28Z","timestamp":1709726788000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6557-123X","authenticated-orcid":false,"given":"Rui","family":"Zha","sequence":"first","affiliation":[{"name":"University of Science and Technology of China,  Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4763-6060","authenticated-orcid":false,"given":"Ying","family":"Sun","sequence":"additional","affiliation":[{"name":"Thrust of Artificial Intelligence, HKUST (Guangzhou),  Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5354-8630","authenticated-orcid":false,"given":"Chuan","family":"Qin","sequence":"additional","affiliation":[{"name":"Career Science Lab, BOSS Zhipin, Beijing, China and PBC School of Finance, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0894-9651","authenticated-orcid":false,"given":"Le","family":"Zhang","sequence":"additional","affiliation":[{"name":"Baidu Research, Baidu Inc.,  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4246-5386","authenticated-orcid":false,"given":"Tong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China,  Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Career Science Lab, BOSS Zhipin, Beijing, China and Thrust of Artificial Intelligence, HKUST (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","volume":"33","author":"Baevski Alexei","year":"2020","unstructured":"Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. 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