{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T01:24:10Z","timestamp":1787189050265,"version":"build-2736575974"},"reference-count":54,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","award":["0095\/2023\/RIA2"],"award-info":[{"award-number":["0095\/2023\/RIA2"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Graph neural networks enhance collaborative signals through multi\u2010hop message passing, achieving promising results in recommender systems. Nevertheless, several challenges still remain. Most existing methods rely solely on user\u2010item interactions, which are insufficient to capture users' fine\u2010grained preferences. Although some approaches exploit aspect information to alleviate this issue, they typically treat aspects as auxiliary information and fail to explicitly integrate them into user (item) embeddings. Additionally, these methods generally treat all unobserved items as negative samples and employ a uniform sampling strategy. However, due to the inherent popularity bias of items, such a negative sampling strategy is suboptimal for effective model training. To address the above issues, we propose a novel method, AEN\u2010GCL, which leverages aspect information to help construct the initial embeddings of users (items) and guide negative sampling, while incorporating a self\u2010supervised auxiliary learning task. Specifically, we first employ a pre\u2010trained Sentence\u2010BERT to compute the similarities between reviews and predefined seed sentences of each aspect. The similarities derived from the reviews of each user (item) are aggregated to form an aspect representation, which is combined with category information to construct the initial embedding. Next, we compute the aspect\u2010level similarity between the user, the positive item and candidate negatives generated by an adaptive category\u2010based strategy. The user\u2010aware and item\u2010aware similarities are jointly used to guide negative sampling. In addition, to alleviate sparse supervision signals, we incorporate edge\u2010dropout\u2010based graph contrastive learning to enhance model training. Comprehensive experiments on three public datasets demonstrate that the proposed AEN\u2010GCL achieves competitive performance compared with state\u2010of\u2010the\u2010art methods.<\/jats:p>","DOI":"10.1111\/exsy.70377","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T11:53:51Z","timestamp":1784721231000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>AEN<\/scp>\n                    \u2010\n                    <scp>GCL<\/scp>\n                    : Aspect\u2010Guided Embedding and Negative Sampling With Graph Contrastive Learning for Recommendation"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9829-2226","authenticated-orcid":false,"given":"Yao","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering Macau University of Science and Technology  Macau China"},{"name":"School of Economics Fuyang Normal University  Fuyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Macau University of Science and Technology  Macau China"},{"name":"College of Computer Information and Engineering Nanchang Institute of Technology  Nanchang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computing Sciences University of East Anglia  Norwich UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuntao","family":"Zou","sequence":"additional","affiliation":[{"name":"Advanced Interdisciplinary Research Center City University of Macau  Macau China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dagang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Macau University of Science and Technology  Macau China"},{"name":"Zhuhai M.U.S.T. Science and Technology Research Institute  Zhuhai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1525"},{"key":"e_1_2_10_3_1","first-page":"321","volume-title":"International Conference of the Italian Association for Artificial Intelligence","author":"\u00c7ak\u0131r M.","year":"2019"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544557"},{"issue":"1","key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3522672","article-title":"Revisiting Negative Sampling vs. Non\u2010Sampling in Implicit Recommendation","volume":"41","author":"Chen C.","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09744-1"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"Gao C. 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