{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T19:37:43Z","timestamp":1773517063743,"version":"3.50.1"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"name":"National Key Research and Development","award":["2022YFB3305602"],"award-info":[{"award-number":["2022YFB3305602"]}]},{"name":"Humanities and Social Sciences Planning Fund of the Ministry of Education","award":["22YJAZH110"],"award-info":[{"award-number":["22YJAZH110"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62272198, No. 62276277"],"award-info":[{"award-number":["No. 62272198, No. 62276277"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A1515010121"],"award-info":[{"award-number":["2024A1515010121"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Cultivation of Guangdong College Students\u2019 Scientific and Technological Innovation","award":["pdjh2025ak028"],"award-info":[{"award-number":["pdjh2025ak028"]}]},{"name":"Cybersecurity College Student Innovation Funding Program"},{"name":"Graduate Students of Jinan University","award":["2025CXY336, 2025CXY339, 2025CXY402"],"award-info":[{"award-number":["2025CXY336, 2025CXY339, 2025CXY402"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities in recommender systems, particularly under the contrastive learning paradigm, where the construction of positive and negative sample pairs effectively captures latent relations between users and items, thereby significantly enhancing recommendation performance. However, existing graph contrastive learning methods predominantly rely on static augmentation strategies, lacking adaptability to diverse user behaviors and semantic structures. Moreover, effectively integrating external knowledge (e.g., user attributes and item semantics) into the contrastive learning process remains a major challenge. To address these limitations, we propose ProGraph, a graph prompt tuning framework tailored for recommendation tasks. ProGraph introduces adaptive contrastive learning within the graph prompt mechanism, enhanced by knowledge-aware guidance, to improve both the discriminability and semantic generalization of learned representations. Specifically, it employs structured prompts to guide GNNs in learning embeddings across multiple semantic subspaces, while incorporating knowledge-assisted graph views to preserve structural consistency and better handle heterogeneous attributes. Unlike traditional full-parameter optimization, ProGraph enables efficient tuning with a small number of learnable prompt parameters, thus achieving better transferability and modular compatibility. Extensive experiments on three real-world recommendation datasets with rich interaction records and knowledge attributes demonstrate that ProGraph consistently outperforms several representative state-of-the-art baselines in top-K recommendation performance.<\/jats:p>","DOI":"10.1145\/3793862","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T14:32:46Z","timestamp":1770215566000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ProGraph: Graph Prompt Tuning with Knowledge-aware Contrastive Learning for Recommendation"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0352-3002","authenticated-orcid":false,"given":"Chuyuan","family":"Wei","sequence":"first","affiliation":[{"name":"Beijing University of Civil Engineering and Architecture, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1716-0673","authenticated-orcid":false,"given":"Anning","family":"He","sequence":"additional","affiliation":[{"name":"Beijing University of Civil Engineering and Architecture, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5610-005X","authenticated-orcid":false,"given":"Shengda","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-559X","authenticated-orcid":false,"given":"Changdong","family":"Wang","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9551-022X","authenticated-orcid":false,"given":"Shuqiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,7]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313705"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583439"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531017"},{"key":"e_1_3_2_5_2","first-page":"52464","article-title":"Universal prompt tuning for graph neural networks","volume":"36","author":"Fang Taoran","year":"2023","unstructured":"Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2023. Universal prompt tuning for graph neural networks. Advances in Neural Information Processing Systems (NeurIPS \u201923) 36 (2023), 52464\u201352489.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS \u201923)"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_2_7_2","unstructured":"Tianyu Gao Adam Fisch and Danqi Chen. 2020. Making pre-trained language models better few-shot learners. arXiv:2012.15723. Retrieved from https:\/\/arxiv.org\/abs\/2012.15723"},{"key":"e_1_3_2_8_2","unstructured":"Yuxian Gu Xu Han Zhiyuan Liu and Minlie Huang. 2021. PPT: Pre-trained prompt tuning for few-shot learning. arXiv:2109.04332. Retrieved from https:\/\/arxiv.org\/abs\/2109.04332"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544107"},{"issue":"4","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2827872","article-title":"The MovieLens datasets: History and context","volume":"5","author":"Harper F. Maxwell","year":"2015","unstructured":"F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 4 (2015), 1\u201319.","journal-title":"ACM Transactions on Interactive Intelligent Systems"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3348537"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3382180"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599768"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_16_2","first-page":"91","volume-title":"Recommender Systems Handbook","author":"Koren Yehuda","year":"2021","unstructured":"Yehuda Koren, Steffen Rendle, and Robert Bell. 2021. Advances in collaborative filtering. In Recommender Systems Handbook. F. Ricci, L. Rokach, and B. Shapira (Eds.), Springer, 91\u2013142."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3573010"},{"issue":"8","key":"e_1_3_2_18_2","first-page":"1","article-title":"SeSMR: Secure and efficient session-based multimedia recommendation in edge computing","author":"Li Fengyin","year":"2024","unstructured":"Fengyin Li, Hongzhe Liu, Guangshun Li, Yilei Wang, Huiyu Zhou, Shanshan Cao, and Tao Li. 2024. SeSMR: Secure and efficient session-based multimedia recommendation in edge computing. ACM Transactions on Multimedia Computing, Communications, and Applications 21, 8 (2024), 1\u201321.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111733"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512104"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"e_1_3_2_23_2","first-page":"157","volume-title":"Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE \u201916)","author":"Qing Lyu Bing","year":"2016","unstructured":"Bing Qing Lyu, Lu Qin, Xuemin Lin, Lijun Chang, and Jeffrey Xu Yu. 2016. Scalable supergraph search in large graph databases. In Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE \u201916). IEEE, 157\u2013168."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482291"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498433"},{"key":"e_1_3_2_26_2","unstructured":"Steffen Rendle Christoph Freudenthaler Zeno Gantner and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618. Retrieved from https:\/\/arxiv.org\/abs\/1205.2618"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Taylor Shin Yasaman Razeghi Robert L. Logan Eric Wallace and Sameer Singh. 2020. AutoPrompt: Eliciting knowledge from language models with automatically generated prompts. arXiv:2010.15980. Retrieved from https:\/\/arxiv.org\/abs\/2010.15980","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657723"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531889"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570483"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271739"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186175"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3418211"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450133"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.17809"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467289"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583206"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413556"},{"issue":"1","key":"e_1_3_2_43_2","first-page":"1","article-title":"Visual background recommendation for dance performances using deep matrix factorization","volume":"14","author":"Wen Jiqing","year":"2018","unstructured":"Jiqing Wen, James She, Xiaopeng Li, and Hui Mao. 2018. Visual background recommendation for dance performances using deep matrix factorization. ACM Transactions on Multimedia Computing, Communications, and Applications 14, 1 (2018), 1\u201319.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"issue":"5","key":"e_1_3_2_45_2","first-page":"4425","article-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","volume":"35","author":"Wu Le","year":"2022","unstructured":"Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4425\u20134445.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_46_2","first-page":"62853","volume-title":"Advances in Neural Information Processing Systems (NeurIPS \u201923)","volume":"36","author":"Yang Haoran","year":"2023","unstructured":"Haoran Yang, Xiangyu Zhao, Yicong Li, Hongxu Chen, and Guandong Xu. 2023. An empirical study towards prompt-tuning for graph contrastive pre-training in recommendations. Advances in Neural Information Processing Systems (NeurIPS \u201923), Vol. 36, 62853\u201362868."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599400"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532009"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591691"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645546"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481952"},{"key":"e_1_3_2_52_2","first-page":"1294","volume-title":"Proceedings of 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922)","author":"Yu Junliang","year":"2022","unstructured":"Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In Proceedings of 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922), 1294\u20131303."},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"issue":"1","key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3701230","article-title":"Simplify to the limit! Embedding-less graph collaborative filtering for recommender systems","volume":"43","author":"Zhang Yi","year":"2024","unstructured":"Yi Zhang, Yiwen Zhang, Lei Sang, and Victor S. Sheng. 2024. Simplify to the limit! Embedding-less graph collaborative filtering for recommender systems. ACM Transactions on Information Systems 43, 1 (2024), 1\u201330.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","unstructured":"Yu Zhang Yiwen Zhang Yi Zhang Lei Sang and Yun Yang. 2025. Unveiling contrastive learning\u2019s capability of neighborhood aggregation for collaborative filtering. arXiv:2504.10113. Retrieved from https:\/\/arxiv.org\/abs\/2504.10113","DOI":"10.1145\/3726302.3730111"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3352573"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463010"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3793862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T13:24:36Z","timestamp":1773494676000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3793862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,7]]},"references-count":56,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4,30]]}},"alternative-id":["10.1145\/3793862"],"URL":"https:\/\/doi.org\/10.1145\/3793862","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,7]]},"assertion":[{"value":"2025-08-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-18","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}