{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:49:08Z","timestamp":1780609748832,"version":"3.54.1"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372264"],"award-info":[{"award-number":["62372264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Kuaishou Inc."}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Recommender systems mitigate information overload by offering personalized suggestions to users. As the interactions between users and items can inherently be depicted as a bipartite graph, recent years have seen a surge in the interest in harnessing graph neural networks (GNNs) for enhancing recommender systems. However, a notable limitation of current GNN-based recommendation models is their exclusive emphasis on positive feedback, while disregarding the potentially insightful negative feedback. In this article, we introduce Pone-GNN, a novel recommendation model that synergistically integrates both\n            <jats:bold>Po<\/jats:bold>\n            sitive and\n            <jats:bold>ne<\/jats:bold>\n            gative feedback in\n            <jats:bold>G<\/jats:bold>\n            raph\n            <jats:bold>N<\/jats:bold>\n            eural\n            <jats:bold>N<\/jats:bold>\n            etworks. Pone-GNN utilizes dual embeddings\u2013interest embeddings for capturing a user\u2019s likes and disinterest embeddings for a user\u2019s dislikes. Also, Pone-GNN performs message passing for both positive and negative feedback, and integrates two sets of embeddings through contrastive learning, which is crucial for extracting robust and discriminative embeddings. Our comprehensive experimental analysis demonstrates that Pone-GNN outperforms state-of-the-art models on diverse real-world recommendation datasets. For example, Pone-GNN achieves a 6.15% increase in relative\n            <jats:italic>nDCG@10<\/jats:italic>\n            compared to the runner-up on the KuaiRec dataset.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3711666","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T09:35:32Z","timestamp":1735983332000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender Systems"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4238-1533","authenticated-orcid":false,"given":"Ziyang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2986-2574","authenticated-orcid":false,"given":"Chaokun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3182-5277","authenticated-orcid":false,"given":"Shuwen","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4481-405X","authenticated-orcid":false,"given":"Cheng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3822-2815","authenticated-orcid":false,"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"Kuaishou, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1714-5527","authenticated-orcid":false,"given":"Yang","family":"Song","sequence":"additional","affiliation":[{"name":"Kuaishou, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0125-1997","authenticated-orcid":false,"given":"Na","family":"Mou","sequence":"additional","affiliation":[{"name":"Kuaishou, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"issue":"3","key":"e_1_3_1_2_2","first-page":"23:1\u201323:40","article-title":"Where are the values? 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