{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T02:56:29Z","timestamp":1771901789088,"version":"3.50.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Laboratory Project of Higher Education Institutions in Shandong Province-Energy System Intelligent Management and Policy Simulation Laboratory at China University of Petroleum"},{"name":"Youth Innovation Team of Higher Education Institutions in Shandong Province-Data Intelligence Innovation Team at China University of Petroleum and Institute for Digital Transformation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Widespread adoption of online recruitment platforms has led to explosive growth in employment information, resulting in an ever-increasing demand from job seekers for accurate and effective job recommendations. Existing studies on the Person-Job Fit models focus on the correlation between resumes and job descriptions, with rare consideration given to user historical behavior such as click and application. On the contrary, job recommendation methods always ignore the crucial information lurking in the resume text. In addition, the continuous influx of a vast amount of job data poses challenges to the updating of online recommendation results. To this end, we propose a novel\n            <jats:italic>O<\/jats:italic>\n            nline\n            <jats:italic>J<\/jats:italic>\n            ob\n            <jats:italic>R<\/jats:italic>\n            ecommendation model\n            <jats:italic>via R<\/jats:italic>\n            esume\n            <jats:italic>F<\/jats:italic>\n            usion (OJRRF) in this article, aimed at making accurate and efficient online job recommendations with the merits of addressing job cold start and long tail problems. The key contribution lies in two facets: (1) incorporating resume text information into the knowledge graph attention framework to enhance job seekers\u2019 vector representations jointly; (2) designing a hybrid recommender strategy by combining the knowledge-aware offline model with the content-based online model. Finally, we conducted extensive comparison experiments and online A\/B test on the recruitment platform of JiuYeJie big data company to validate the effectiveness and real-time capability of OJRRF. The release code can be found in\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/urnotada\/OJRRF\">https:\/\/github.com\/urnotada\/OJRRF<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3716503","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T14:58:21Z","timestamp":1738940301000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Together Is Better: Knowledge-aware Model with Resume Fusion for Online Job Recommendation"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9787-1424","authenticated-orcid":false,"given":"Xiao","family":"Gu","sequence":"first","affiliation":[{"name":"Shandong Academy of Social Sciences, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9385-5977","authenticated-orcid":false,"given":"Ling","family":"Jian","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China), Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5447-9027","authenticated-orcid":false,"given":"Chongzhi","family":"Rao","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China), Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5462-2184","authenticated-orcid":false,"given":"Zhaohui","family":"Bu","sequence":"additional","affiliation":[{"name":"Qingdao Jiuyejie Big Data Technology Co., Ltd, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2431-2273","authenticated-orcid":false,"given":"Xianggang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Qingdao Jiuyejie Big Data Technology Co., Ltd, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"issue":"1","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1109\/TCSS.2021.3134458","article-title":"Self-attentional multi-field features representation and interaction learning for person-job fit","volume":"10","author":"He M.","year":"2021","unstructured":"M. He, D. Shen, T. Wang, H. Zhao, Z. Zhang, and R. He. 2021. Self-attentional multi-field features representation and interaction learning for person-job fit. IEEE Transactions on Computational Social Systems 10, 1 (2021), 255\u2013268.","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118459"},{"key":"e_1_3_1_4_2","first-page":"974","volume-title":"ACM SIGKDD","author":"Ying R.","year":"2018","unstructured":"R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In ACM SIGKDD, 974\u2013983."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210025"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411929"},{"key":"e_1_3_1_7_2","first-page":"102","article-title":"Modeling two-way selection preference for person-job fit","author":"Yang C.","year":"2022","unstructured":"C. Yang, Y. Hou, Y. Song, T. Zhang, J.-R. Wen, and W. X. Zhao. 2022. Modeling two-way selection preference for person-job fit. In RecSys, 102\u2013112.","journal-title":"RecSys"},{"key":"e_1_3_1_8_2","first-page":"914","article-title":"Interview choice reveals your preference on the market: To improve job-resume matching through profiling memories","author":"Yan R.","year":"2019","unstructured":"R. Yan, R. Le, Y. Song, T. Zhang, X. Zhang, and D. Zhao. 2019. Interview choice reveals your preference on the market: To improve job-resume matching through profiling memories. In ACM SIGKDD, 914\u2013922.","journal-title":"ACM SIGKDD"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"3571","DOI":"10.1109\/BigData52589.2021.9672057","volume-title":"2021 IEEE International Conference on Big Data","author":"Zhang Y.","year":"2021","unstructured":"Y. Zhang, B. Liu, J. Qian, J. Qin, X. Zhang, and X. Jiang. 2021. An explainable person-job fit model incorporating structured information. In 2021 IEEE International Conference on Big Data, 3571\u20133579."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3028705"},{"key":"e_1_3_1_11_2","article-title":"Learning entity and relation embeddings for knowledge graph completion","volume":"29","author":"Lin Y.","year":"2015","unstructured":"Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI, Vol. 29.","journal-title":"AAAI"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.5555\/1768197.1768209"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"},{"key":"e_1_3_1_15_2","first-page":"353","article-title":"Collaborative knowledge base embedding for recommender systems","author":"Zhang F.","year":"2016","unstructured":"F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma. 2016. Collaborative knowledge base embedding for recommender systems. In SIGKDD, 353\u2013362.","journal-title":"SIGKDD"},{"key":"e_1_3_1_16_2","unstructured":"T. N. Kipf and M. Welling. 2016. Semi-supervised classification with graph convolutional networks. arxiv:1609.02907. Retrieved from https:\/\/arxiv.org\/abs\/1609.02907v4"},{"key":"e_1_3_1_17_2","first-page":"417","volume-title":"CIKM","author":"Wang H.","year":"2018","unstructured":"H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo. 2018. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In CIKM, 417\u2013426."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_1_19_2","first-page":"878","volume-title":"Web Conference","author":"Wang X.","year":"2021","unstructured":"X. Wang, T. Huang, D. Wang, Y. Yuan, Z. Liu, X. He, and T.-S. Chua. 2021. Learning intents behind interactions with knowledge graph for recommendation. In Web Conference, 878\u2013887."},{"key":"e_1_3_1_20_2","first-page":"1390","volume-title":"ACM SIGIR","author":"Du Y.","year":"2022","unstructured":"Y. Du, X. Zhu, L. Chen, B. Zheng, and Y. Gao. 2022. Hakg: Hierarchy-aware knowledge gated network for recommendation. In ACM SIGIR, 1390\u20131400."},{"issue":"3","key":"e_1_3_1_21_2","first-page":"1","article-title":"Person-job fit: Adapting the right talent for the right job with joint representation learning","volume":"9","author":"Zhu C.","year":"2018","unstructured":"C. Zhu, H. Zhu, H. Xiong, C. Ma, F. Xie, P. Ding, and P. Li. 2018. Person-job fit: Adapting the right talent for the right job with joint representation learning. ACM (TMIS) 9, 3 (2018), 1\u201317.","journal-title":"ACM (TMIS)"},{"key":"e_1_3_1_22_2","first-page":"404","article-title":"Textrank: Bringing order into text","author":"Mihalcea R.","year":"2004","unstructured":"R. Mihalcea and P. Tarau. 2004. Textrank: Bringing order into text. In NLP, 404\u2013411.","journal-title":"NLP"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"issue":"10","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"9904","DOI":"10.1109\/TKDE.2022.3194952","article-title":"Tag: Joint triple-hierarchical attention and gcn for review-based social recommender system","volume":"35","author":"Qiao P.","year":"2022","unstructured":"P. Qiao, Z. Zhang, Z. Li, Y. Zhang, K. Bian, Y. Li, and G. Wang. 2022. Tag: Joint triple-hierarchical attention and gcn for review-based social recommender system. IEEE Transactions on Knowledge and Data Engineering 35, 10 (2022), 9904\u20139919.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_25_2","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton W.","year":"2017","unstructured":"W. Hamilton, Z. Ying, and J. Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"20","key":"e_1_3_1_26_2","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Veli\u010dkovi\u0107 P.","year":"2017","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2017. Graph attention networks. Stat 1050, 20 (2017), 10\u201348550.","journal-title":"Stat"},{"key":"e_1_3_1_27_2","first-page":"3307","volume-title":"WWW","author":"Wang H.","year":"2019","unstructured":"H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo. 2019. Knowledge graph convolutional networks for recommender systems. In WWW, 3307\u20133313."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548273"},{"key":"e_1_3_1_29_2","first-page":"3046","volume-title":"ACM SIGKDD","author":"Yang Y.","year":"2023","unstructured":"Y. Yang, C. Huang, L. Xia, and C. Huang. 2023. Knowledge graph self-supervised rationalization for recommendation. In ACM SIGKDD, 3046\u20133056."},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357949"},{"key":"e_1_3_1_31_2","first-page":"346","article-title":"Personalized job recommendation system at linkedin: Practical challenges and lessons learned","author":"Kenthapadi K.","year":"2017","unstructured":"K. Kenthapadi, B. Le, and G. Venkataraman. 2017. Personalized job recommendation system at linkedin: Practical challenges and lessons learned. In RecSys, 346\u2013347.","journal-title":"RecSys"},{"issue":"5","key":"e_1_3_1_32_2","first-page":"5341","article-title":"Towards automatic job description generation with capability-aware neural networks","volume":"35","author":"Qin C.","year":"2022","unstructured":"C. Qin, K. Yao, H. Zhu, T. Xu, D. Shen, E. Chen, and H. Xiong. 2022. Towards automatic job description generation with capability-aware neural networks. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 5341\u20135355.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324916000334"},{"key":"e_1_3_1_34_2","first-page":"1188","volume-title":"ICML","author":"Le Q.","year":"2014","unstructured":"Q. Le and T. Mikolov. 2014. Distributed representations of sentences and documents. In ICML, 1188\u20131196."},{"key":"e_1_3_1_35_2","first-page":"219","article-title":"Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems","author":"Neve J.","year":"2019","unstructured":"J. Neve and I. Palomares. 2019. Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems. In RecSys, 219\u2013227.","journal-title":"RecSys"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716503","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3716503","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:10Z","timestamp":1750295950000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3716503"],"URL":"https:\/\/doi.org\/10.1145\/3716503","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"2024-03-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}