{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:02:36Z","timestamp":1775815356254,"version":"3.50.1"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U23A20305 and 62302345"],"award-info":[{"award-number":["U23A20305 and 62302345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Recently, the paradigm of pre-training and fine-tuning has achieved impressive performance owing to their ability to transfer general knowledge from pre-trained domain to target domain. Meanwhile,\n            <jats:bold>graph neural networks (GNNs)<\/jats:bold>\n            have gained prominence in recommender systems. However, there is a lack of unified pre-training and fine-tuning paradigms in graph-based recommendation systems. Applying pre-training and fine-tuning in graph-based recommendation is challenging due to the unique characteristics of recommendation data, including the non-uniform representation, negative transfer effects, and skewed data distributions. To overcome these challenges, we introduce\n            <jats:bold>\n              pre-training and prompting recommendation (\n              <jats:sc>ProRec<\/jats:sc>\n              )\n            <\/jats:bold>\n            , a novel model that synergizes uniform graph pre-training with prompt-tuning for recommendation systems. Specifically, to address the challenge of inconsistent features across different recommendation datasets,\n            <jats:sc>ProRec<\/jats:sc>\n            constructs unified input features at the subgraph level and uses a graph auto-encoder for pre-training, laying the foundation for uniform knowledge transfer from the pre-trained domain to the downstream domain. Additionally,\n            <jats:sc>ProRec<\/jats:sc>\n            employs prompt-tuning during the fine-tuning phase, which, in a parameter-efficient manner, enhances the generalization of pre-trained knowledge to downstream tasks thereby reducing negative transfer effects. Furthermore, a cross-layer contrastive learning strategy is adopted to eliminate uneven data distribution, promoting more evenly distributed and informative representations. Finally, extensive benchmark comparisons have demonstrated that\n            <jats:sc>ProRec<\/jats:sc>\n            outperforms the latest state-of-the-art methods. The source code necessary for replication is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Code2Q\/ProRec\">https:\/\/github.com\/Code2Q\/ProRec<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3724392","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T13:44:21Z","timestamp":1742305461000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Uniform Graph Pre-training and Prompting for Transferable\u00a0Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4041-8390","authenticated-orcid":false,"given":"Qing","family":"Yu","sequence":"first","affiliation":[{"name":"Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6755-871X","authenticated-orcid":false,"given":"Lixin","family":"Zou","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-2950","authenticated-orcid":false,"given":"Xiangyang","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Lab of Mathematical Engineering and Advanced Computing, Zhengzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-4416","authenticated-orcid":false,"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-6374","authenticated-orcid":false,"given":"Chenliang","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Hangbo Bao Li Dong Songhao Piao and Furu Wei. 2021. 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