{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T08:41:38Z","timestamp":1780562498487,"version":"3.54.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Graduate Innovation Program of China University of Mining and Technology","award":["2024WLKXJ183"],"award-info":[{"award-number":["2024WLKXJ183"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2024-10949"],"award-info":[{"award-number":["2024-10949"]}]},{"name":"the Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX24_2781"],"award-info":[{"award-number":["KYCX24_2781"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security","award":["MIMS24-13"],"award-info":[{"award-number":["MIMS24-13"]}]},{"name":"National Natural Science Foundation of China under Grant","award":["71774159"],"award-info":[{"award-number":["71774159"]}]},{"name":"China Postdoctoral Science Foundation under Grant","award":["2021T140707"],"award-info":[{"award-number":["2021T140707"]}]},{"name":"Jiangsu Postdoctoral Science Foundation under grant","award":["2021K565C"],"award-info":[{"award-number":["2021K565C"]}]},{"name":"the Science and Technology Foundation of Xuzhou under Grant","award":["KC22047"],"award-info":[{"award-number":["KC22047"]}]},{"name":"Guangxi Key Laboratory of Big Data in Finance and Economics","award":["FEDOP2022A03"],"award-info":[{"award-number":["FEDOP2022A03"]}]},{"name":"Australian Research Council under Grant","award":["DP230101122"],"award-info":[{"award-number":["DP230101122"]}]},{"name":"Australian Research Council under Grant","award":["DP230101122"],"award-info":[{"award-number":["DP230101122"]}]},{"name":"the Project of Guangxi Science and Technology","award":["GuiKeAB23026040"],"award-info":[{"award-number":["GuiKeAB23026040"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10115-025-02404-7","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T03:10:36Z","timestamp":1744427436000},"page":"5999-6020","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deconfounding representation learning for mitigating latent confounding effects in recommendation"],"prefix":"10.1007","volume":"67","author":[{"given":"Guixian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debo","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"2404_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106196","volume":"204","author":"L Wang","year":"2020","unstructured":"Wang L, Zhang X, Wang R, Yan C, Kou H, Qi L (2020) Diversified service recommendation with high accuracy and efficiency. Knowl-Based Syst 204:106196","journal-title":"Knowl-Based Syst"},{"key":"2404_CR2","doi-asserted-by":"crossref","unstructured":"Elahi M, Jannach D, Skj\u00e6rven L, Knudsen E, Sj\u00f8vaag H, Tolonen K, Holmstad \u00d8, Pipkin I, Throndsen E, Stenbom A et al (2022) Towards responsible media recommendation. AI Ethics 1\u201312","DOI":"10.1007\/s43681-021-00107-7"},{"issue":"5","key":"2404_CR3","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1007\/s10115-022-01811-4","volume":"65","author":"X Fu","year":"2023","unstructured":"Fu X, Li J, Wu J, Qin J, Sun Q, Ji C, Wang S, Peng H, Yu PS (2023) Adaptive curvature exploration geometric graph neural network. Knowl Inf Syst 65(5):2281\u20132304","journal-title":"Knowl Inf Syst"},{"key":"2404_CR4","doi-asserted-by":"crossref","unstructured":"Chen X, Fan W, Chen J, Liu H, Liu Z, Zhang Z, Li Q (2023) Fairly adaptive negative sampling for recommendations. In: Proceedings of the ACM web conference 2023, pp 3723\u20133733","DOI":"10.1145\/3543507.3583355"},{"key":"2404_CR5","doi-asserted-by":"crossref","unstructured":"Wang Y, Liang D, Charlin L, Blei DM (2020) Causal inference for recommender systems. In: Proceedings of the 14th ACM conference on recommender systems, pp 426\u2013431","DOI":"10.1145\/3383313.3412225"},{"issue":"3","key":"2404_CR6","first-page":"1","volume":"8","author":"S Zhang","year":"2017","unstructured":"Zhang S, Li X, Zong M, Zhu X, Cheng D (2017) Learning k for KNN classification. ACM Trans Intell Syst Technol (TIST) 8(3):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"2404_CR7","unstructured":"Yu D, Li Q, Yin H, Xu G (2023) Causality-guided graph learning for session-based recommendation. In: Proceedings of the 32nd ACM international conference on information and knowledge management, pp 3083\u20133093"},{"key":"2404_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2023.3290544","volume":"01","author":"T Cao","year":"2023","unstructured":"Cao T, Xu Q, Yang Z, Huang Q (2023) Mitigating confounding bias in practical recommender systems with partially inaccessible exposure status. IEEE Trans Pattern Anal Mach Intell 01:1\u201318","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"2404_CR9","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s10115-023-01986-4","volume":"66","author":"J Chen","year":"2024","unstructured":"Chen J, Li H, Zhang X, Zhang F, Wang S, Wei K, Ji J (2024) Sr-HetGNN: session-based recommendation with heterogeneous graph neural network. Knowl Inf Syst 66(2):1111\u20131134","journal-title":"Knowl Inf Syst"},{"key":"2404_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl J (2009) Causality. Cambridge University Press, Cambridge"},{"issue":"7","key":"2404_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3643890","volume":"18","author":"Z Ling","year":"2024","unstructured":"Ling Z, Xu E, Zhou P, Du L, Yu K, Wu X (2024) Fair feature selection: a causal perspective. ACM Trans Knowl Discov Data 18(7):1\u201323","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"1","key":"2404_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103570","volume":"61","author":"G Zhang","year":"2024","unstructured":"Zhang G, Cheng D, Yuan G, Zhang S (2024) Learning fair representations via rebalancing graph structure. Inf Process Manag 61(1):103570","journal-title":"Inf Process Manag"},{"issue":"4","key":"2404_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3606035","volume":"1","author":"S Xu","year":"2023","unstructured":"Xu S, Tan J, Heinecke S, Li VJ, Zhang Y (2023) Deconfounded causal collaborative filtering. ACM Trans Recomm Syst 1(4):1\u201325","journal-title":"ACM Trans Recomm Syst"},{"key":"2404_CR14","doi-asserted-by":"crossref","unstructured":"Wang W, Feng F, He X, Wang X, Chua T-S (2021) Deconfounded recommendation for alleviating bias amplification. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 1717\u20131725","DOI":"10.1145\/3447548.3467249"},{"key":"2404_CR15","doi-asserted-by":"crossref","unstructured":"Chaney AJ, Stewart BM, Engelhardt BE (2018) How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In: Proceedings of the 12th ACM conference on recommender systems, pp 224\u2013232","DOI":"10.1145\/3240323.3240370"},{"key":"2404_CR16","doi-asserted-by":"crossref","unstructured":"Bonner S, Vasile F (2018) Causal embeddings for recommendation. In: Proceedings of the 12th ACM conference on recommender systems, pp 104\u2013112","DOI":"10.1145\/3240323.3240360"},{"key":"2404_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106781","volume":"181","author":"G Zhang","year":"2025","unstructured":"Zhang G, Yuan G, Cheng D, Liu L, Li J, Zhang S (2025) Disentangled contrastive learning for fair graph representations. Neural Netw 181:106781","journal-title":"Neural Netw"},{"key":"2404_CR18","unstructured":"Wang T-Z, Qin T, Zhou Z-H (2023) Estimating possible causal effects with latent variables via adjustment. In: International conference on machine learning. PMLR, pp 36308\u201336335"},{"key":"2404_CR19","doi-asserted-by":"crossref","unstructured":"Cheng D, Li J, Liu L, Xu Z, Zhang W, Liu J, Le TD (2024) Disentangled representation learning for causal inference with instruments. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2024.3512790"},{"key":"2404_CR20","doi-asserted-by":"crossref","unstructured":"Krishnan A, Sharma A, Sankar A, Sundaram H (2018) An adversarial approach to improve long-tail performance in neural collaborative filtering. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1491\u20131494","DOI":"10.1145\/3269206.3269264"},{"key":"2404_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Feng F, He X, Wei T, Song C, Ling G, Zhang Y (2021) Causal intervention for leveraging popularity bias in recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 11\u201320","DOI":"10.1145\/3404835.3462875"},{"issue":"12","key":"2404_CR22","doi-asserted-by":"publisher","first-page":"17123","DOI":"10.1109\/TNNLS.2023.3299929","volume":"35","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, Qin J, Wei P, Chen Z, Lin L (2023) CIPL: counterfactual interactive policy learning to eliminate popularity bias for online recommendation. IEEE Trans Neural Netw Learn Syst 35(12):17123\u201317136","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2404_CR23","doi-asserted-by":"crossref","unstructured":"Agarwal A, Zaitsev I, Wang X, Li C, Najork M, Joachims T (2019) Estimating position bias without intrusive interventions. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 474\u2013482","DOI":"10.1145\/3289600.3291017"},{"key":"2404_CR24","doi-asserted-by":"crossref","unstructured":"Jeunen O (2023) A probabilistic position bias model for short-video recommendation feeds. In: Proceedings of the 17th ACM conference on recommender systems, pp 675\u2013681","DOI":"10.1145\/3604915.3608777"},{"key":"2404_CR25","doi-asserted-by":"crossref","unstructured":"Wang Y, Xue Y, Liu B, Wen M, Zhao W, Guo S, Yu PS (2023) Click-conversion multi-task model with position bias mitigation for sponsored search in ecommerce. In: Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pp 1884\u20131888","DOI":"10.1145\/3539618.3591963"},{"issue":"2","key":"2404_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624986","volume":"18","author":"R Cai","year":"2023","unstructured":"Cai R, Wu F, Li Z, Qiao J, Chen W, Hao Y, Gu H (2023) Rest: debiased social recommendation via reconstructing exposure strategies. ACM Trans Knowl Discov Data 18(2):1\u201324","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2404_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.patrec.2018.01.013","volume":"109","author":"R Wang","year":"2018","unstructured":"Wang R, Ji W, Liu M, Wang X, Weng J, Deng S, Gao S, Yuan C-A (2018) Review on mining data from multiple data sources. Pattern Recognit Lett 109:120\u2013128","journal-title":"Pattern Recognit Lett"},{"key":"2404_CR28","doi-asserted-by":"crossref","unstructured":"Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X (2021) Self-supervised graph learning for recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 726\u2013735","DOI":"10.1145\/3404835.3462862"},{"key":"2404_CR29","doi-asserted-by":"crossref","unstructured":"Huang T, Dong Y, Ding M, Yang Z, Feng W, Wang X, Tang J (2021) MixGCF: an improved training method for graph neural network-based recommender systems. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 665\u2013674","DOI":"10.1145\/3447548.3467408"},{"key":"2404_CR30","doi-asserted-by":"crossref","unstructured":"Lin Z, Tian C, Hou Y, Zhao WX (2022) Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: Proceedings of the ACM web conference 2022, pp 2320\u20132329","DOI":"10.1145\/3485447.3512104"},{"key":"2404_CR31","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Xia X, Chen T, Cui L, Nguyen QVH (2022) Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1294\u20131303","DOI":"10.1145\/3477495.3531937"},{"key":"2404_CR32","doi-asserted-by":"crossref","unstructured":"Wang C, Yu Y, Ma W, Zhang M, Chen C, Liu Y, Ma S (2022) Towards representation alignment and uniformity in collaborative filtering. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 1816\u20131825","DOI":"10.1145\/3534678.3539253"},{"key":"2404_CR33","unstructured":"Cai X, Huang C, Xia L, Ren X (2023) LightGCL: simple yet effective graph contrastive learning for recommendation. In: Proceedings of the 11th international conference on learning representations"},{"key":"2404_CR34","unstructured":"Gupta S, Wang H, Lipton Z, Wang Y (2021) Correcting exposure bias for link recommendation. In: International conference on machine learning. PMLR, pp 3953\u20133963"},{"key":"2404_CR35","doi-asserted-by":"crossref","unstructured":"Xie R, Zhang S, Wang R, Xia F, Lin L (2021) Hierarchical reinforcement learning for integrated recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4521\u20134528","DOI":"10.1609\/aaai.v35i5.16580"},{"key":"2404_CR36","doi-asserted-by":"crossref","unstructured":"Liang Y, Yang E, Guo G, Cai W, Jiang L, Wang X (2024) Deconfounding user preference in recommendation systems through implicit and explicit feedback. ACM Trans Knowl Discov Data","DOI":"10.1145\/3673762"},{"issue":"3","key":"2404_CR37","first-page":"1","volume":"41","author":"J Chen","year":"2023","unstructured":"Chen J, Dong H, Wang X, Feng F, Wang M, He X (2023) Bias and debias in recommender system: a survey and future directions. ACM Trans Inf Syst 41(3):1\u201339","journal-title":"ACM Trans Inf Syst"},{"key":"2404_CR38","doi-asserted-by":"crossref","unstructured":"Zhang Q, Xia L, Cai X, Yiu S, Huang C, Jensen CS (2024) Graph augmentation for recommendation. In: 2024 IEEE 40th international conference on data engineering (ICDE). IEEE","DOI":"10.1109\/ICDE60146.2024.00049"},{"issue":"5","key":"2404_CR39","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","volume":"29","author":"S Zhang","year":"2017","unstructured":"Zhang S, Li X, Zong M, Zhu X, Wang R (2017) Efficient KNN classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 29(5):1774\u20131785","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2404_CR40","doi-asserted-by":"crossref","unstructured":"Deng J, Chen X, Jiang R, Yin D, Yang Y, Song X, Tsang IW (2024) Disentangling structured components: Towards adaptive, interpretable and scalable time series forecasting. IEEE Trans Knowl Data Eng (01):1\u201318","DOI":"10.1109\/TKDE.2024.3371931"},{"issue":"1","key":"2404_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605894","volume":"18","author":"J Deng","year":"2023","unstructured":"Deng J, Deng J, Yin D, Jiang R, Song X (2023) TTS-Norm: forecasting tensor time series via multi-way normalization. ACM Trans Knowl Discov Data 18(1):1\u201325","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2404_CR42","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.neucom.2022.06.082","volume":"503","author":"S Zhang","year":"2022","unstructured":"Zhang S, Li J, Zhang W, Qin Y (2022) Hyper-class representation of data. Neurocomputing 503:200\u2013218","journal-title":"Neurocomputing"},{"issue":"10","key":"2404_CR43","doi-asserted-by":"publisher","first-page":"4663","DOI":"10.1109\/TKDE.2021.3049250","volume":"34","author":"S Zhang","year":"2022","unstructured":"Zhang S (2022) Challenges in KNN classification. IEEE Trans Knowl Data Eng 34(10):4663\u20134675","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2404_CR44","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Li J, Wang Q, Hung NQV, Zhang X (2021) Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Proceedings of the web conference 2021, pp 413\u2013424","DOI":"10.1145\/3442381.3449844"},{"key":"2404_CR45","doi-asserted-by":"crossref","unstructured":"Xia L, Huang C, Zhang C (2022) Self-supervised hypergraph transformer for recommender systems. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 2100\u20132109","DOI":"10.1145\/3534678.3539473"},{"issue":"2","key":"2404_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3591469","volume":"1","author":"X Zhou","year":"2023","unstructured":"Zhou X, Sun A, Liu Y, Zhang J, Miao C (2023) SelfCF: a simple framework for self-supervised collaborative filtering. ACM Trans Recomm Syst 1(2):1\u201325","journal-title":"ACM Trans Recomm Syst"},{"issue":"2","key":"2404_CR47","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/TMI.2022.3201974","volume":"42","author":"L Peng","year":"2022","unstructured":"Peng L, Wang N, Xu J, Zhu X, Li X (2022) Gate: Graph CCA for temporal self-supervised learning for label-efficient FMRI analysis. IEEE Trans Med Imaging 42(2):391\u2013402","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"2404_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450528","volume":"15","author":"J Deng","year":"2021","unstructured":"Deng J, Chen X, Fan Z, Jiang R, Song X, Tsang IW (2021) The pulse of urban transport: exploring the co-evolving pattern for spatio-temporal forecasting. ACM Trans Knowl Discov Data (TKDD) 15(6):1\u201325","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"2404_CR49","unstructured":"Veli\u010dkovi\u0107 P, Fedus W, Hamilton WL, Li\u00f2 P, Bengio Y, Hjelm RD (2019) Deep graph infomax. In: Proceedings of the 7th international conference on learning representations"},{"key":"2404_CR50","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th international conference on machine learning, pp 1597\u20131607"},{"key":"2404_CR51","unstructured":"Oord Avd, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. Preprint at arXiv:1807.03748"},{"key":"2404_CR52","unstructured":"Hjelm RD, Fedorov A, Lavoie-Marchildon S, Grewal K, Bachman P, Trischler A, Bengio Y (2019) Learning deep representations by mutual information estimation and maximization. In: Proceedings of the 7th international conference on learning representations"},{"key":"2404_CR53","unstructured":"Tian Y, Sun C, Poole B, Krishnan D, Schmid C, Isola P (2020) What makes for good views for contrastive learning? In: Proceedings of the 34th international conference on neural information processing systems, pp 6827\u20136839"},{"key":"2404_CR54","unstructured":"Xu J, Chen S, Ren Y, Shi X, Shen H, Niu G, Zhu X (2023) Self-weighted contrastive learning among multiple views for mitigating representation degeneration. In: Proceedings of the 37th international conference on neural information processing systems, pp 1119\u20131131"},{"key":"2404_CR55","unstructured":"Wang T, Isola P (2020) Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: Proceedings of the 37th international conference on machine learning, pp 9929\u20139939"},{"key":"2404_CR56","unstructured":"Saunshi N, Ash J, Goel S, Misra D, Zhang C, Arora S, Kakade S, Krishnamurthy A (2022) Understanding contrastive learning requires incorporating inductive biases. In: Proceedings of the 39th international conference on machine learning, pp 19250\u201319286"},{"key":"2404_CR57","unstructured":"Guo X, Wang Y, Wei Z, Wang Y (2023) Architecture matters: uncovering implicit mechanisms in graph contrastive learning. In: Proceedings of the 37th international conference on neural information processing systems, pp.28585\u201328610"},{"key":"2404_CR58","first-page":"1","volume":"62","author":"R Guan","year":"2024","unstructured":"Guan R, Li Z, Tu W, Wang J, Liu Y, Li X, Tang C, Feng R (2024) Contrastive multiview subspace clustering of hyperspectral images based on graph convolutional networks. IEEE Trans Geosci Remote Sens 62:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"2404_CR59","first-page":"1","volume":"55","author":"Y Bao","year":"2025","unstructured":"Bao Y, Shen Q, Cao Y, Shi Q (2025) Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction. Appl Intell 55(6):1\u201319","journal-title":"Appl Intell"},{"issue":"4","key":"2404_CR60","first-page":"1","volume":"18","author":"G Zhang","year":"2024","unstructured":"Zhang G, Zhang S, Yuan G (2024) Bayesian graph local extrema convolution with long-tail strategy for misinformation detection. ACM Trans Knowl Discov Data 18(4):1\u201321","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2404_CR61","unstructured":"Chen J, Liu H, Hopcroft JE, He K (2024) Leveraging contrastive learning for enhanced node representations in tokenized graph transformers. In: Proceedings of the 38th annual conference on neural information processing systems, vol 37, pp 85824\u201385845"},{"key":"2404_CR62","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"2404_CR63","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"issue":"1","key":"2404_CR64","first-page":"1","volume":"18","author":"H Liu","year":"2023","unstructured":"Liu H, Zhang Y, Li P, Qian C, Zhao P, Wu X (2023) DeepCPR: deep path reasoning using sequence of user-preferred attributes for conversational recommendation. ACM Trans Knowl Discov Data 18(1):1\u201322","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2404_CR65","unstructured":"Liu H, Zhang H, Li P, Zhao P, Wu X (2024) Denoising implicit feedback for graph collaborative filtering via causal intervention. IEEE Trans Big Data 1\u201314"},{"key":"2404_CR66","doi-asserted-by":"crossref","unstructured":"Zhang X, Yu FX, Kumar S, Chang S-F (2017) Learning spread-out local feature descriptors. In: Proceedings of the IEEE international conference on computer vision, pp 4595\u20134603","DOI":"10.1109\/ICCV.2017.492"},{"key":"2404_CR67","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pp 452\u2013461"},{"issue":"4","key":"2404_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper FM, Konstan JA (2015) The MovieLens datasets: history and context. ACM Trans Interact Intell Syst 5(4):1\u201319","journal-title":"ACM Trans Interact Intell Syst"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02404-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02404-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02404-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T11:54:14Z","timestamp":1750074854000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02404-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["2404"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02404-7","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,12]]},"assertion":[{"value":"24 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}