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Inf. Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Graph neural networks (GNNs) are commonly used and have shown promising performance in recommendation systems. A major branch, heterogeneous GNNs, models heterogeneous information by leveraging side information for academic paper recommendations. These networks use message passing and high-order propagation to learn representations for users and items. However, existing recommendation methods perform high-order propagation, leading to sub-optimal representation learning. To address this issue, this article proposes a framework called MCAP, which uses relation-aware GNNs and executes low-pass propagation with matrix completion to enhance academic paper recommendations. The framework uses an attention mechanism to learn top-\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(U\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            relationships by constructing a user\u2013user relation graph based on common authors and venues from interacted items. To efficiently and effectively capture semantic-aware similar items, MCAP builds an item\u2013item relation graph by fusing side information of papers using text embedding models (e.g., Mistral) and large language models (e.g., GPT-3.5-Turbo, GLM-4). Finally, the relation-aware user\u2013user and item\u2013item graphs are incorporated into existing GNN-based models to generate representations of users and papers to enhance academic paper recommendations. The effectiveness of the MCAP is validated using four academic datasets, AMiner-PC, AMiner-WeChat, CiteULike, and DBLP, with user\u2013item interactions and side information of papers. Comprehensive experiments show that the MCAP outperforms state-of-the-art models in terms of Recall@5, NDCG@5, and HR@5 with 69.2%, 70.5%, and 77.6% on the AMiner-WeChat dataset. The code for MCAP is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/THUDM\/MCAP\">https:\/\/github.com\/THUDM\/MCAP<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3698193","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T15:05:33Z","timestamp":1727795133000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MCAP: Low-Pass GNNs with Matrix Completion for Academic Recommendations"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1115-3945","authenticated-orcid":false,"given":"Dan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7833-1497","authenticated-orcid":false,"given":"Shaojie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Yanshan University, Qinghuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7695-1633","authenticated-orcid":false,"given":"Yifan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1936-6323","authenticated-orcid":false,"given":"Huihui","family":"Yuan","sequence":"additional","affiliation":[{"name":"Zhipu.AI, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4449-5845","authenticated-orcid":false,"given":"Jibing","family":"Gong","sequence":"additional","affiliation":[{"name":"Yanshan University, Qinghuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3487-4593","authenticated-orcid":false,"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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