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Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    The proliferation of online publications and interdisciplinary studies has presented researchers with the challenge of sifting through a substantial volume of articles to identify citations that substantiate their research ideas. Consequently, the development of citation recommendation technology has become a pivotal aspect of product promotion and marketing for academic support platforms. Traditionally, citation recommendation has primarily relied either on collaborative signals derived from paper interactions or on content-based similarity\u2014both of which are essential for identifying relevant references. However, existing basic strategies often focus on one of these aspects while neglecting the other, leading to suboptimal performance in capturing the complex factors behind citation behavior. The reason is that the integration of domain characteristics in scholarly fields and the mining of semantic relevance in text information is also crucial for modelling researchers\u2019 preferences. In this work, we present a novel citation recommendation model called SCTRec, that aligns\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    equential\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    ollaborative signals from publications\u2019 indexes, that is, IDs, and\n                    <jats:italic toggle=\"yes\">T<\/jats:italic>\n                    ext semantic content for citation\n                    <jats:italic toggle=\"yes\">Rec<\/jats:italic>\n                    ommendation. To address the actual technical challenges encountered, such as shifts in user preferences and semantic gaps between IDs and texts, we have designed a hybrid enhancement mechanism that bridges these semantic gaps, thereby learning more discriminative feature representations. The effectiveness of SCTRec in enhancing citation recommendation performance is substantiated by extensive experimental evaluation on multiple public datasets. The code is available on Anonymous Github at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/guaiqihen\/SCTRec\">https:\/\/github.com\/guaiqihen\/SCTRec<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3797954","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T14:06:33Z","timestamp":1771855593000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Align Sequential Collaborative Signals and Text Semantics for Citation Recommendation: A Hybrid Perspective"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6171-0511","authenticated-orcid":false,"given":"Jiahui","family":"Wang","sequence":"first","affiliation":[{"name":"College of Management and Economics, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0669-179X","authenticated-orcid":false,"given":"Ning","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2575-4779","authenticated-orcid":false,"given":"Likang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3099-4803","authenticated-orcid":false,"given":"Hongke","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4556-0581","authenticated-orcid":false,"given":"Le","family":"Wu","sequence":"additional","affiliation":[{"name":"Innovation School of Artificial Intelligence, Hefei University of Technology, Hefei, China and Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06135-y"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/ICEEOT.2016.7754750","volume-title":"Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","author":"Bafna Prafulla","year":"2016","unstructured":"Prafulla Bafna, Dhanya Pramod, and Anagha Vaidya. 2016. 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