{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T01:38:30Z","timestamp":1783474710062,"version":"3.55.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science Fund for Outstanding Youth of Xinjiang Uygur Autonomous Region","award":["No.2021D01E14"],"award-info":[{"award-number":["No.2021D01E14"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00521-024-09795-8","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T18:02:19Z","timestamp":1714672939000},"page":"13989-14003","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8858-7736","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwei","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"issue":"5","key":"9795_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3535101","volume":"55","author":"S Wu","year":"2022","unstructured":"Wu S et al (2022) Graph neural networks in recommender systems: a survey. ACM Comput Surv 55(5):1\u201337","journal-title":"ACM Comput Surv"},{"key":"9795_CR2","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"9795_CR3","doi-asserted-by":"crossref","unstructured":"Sarwar B et al (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, pp 285\u2013295","DOI":"10.1145\/371920.372071"},{"key":"9795_CR4","unstructured":"Berg Rvd, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv:1706.02263"},{"key":"9795_CR5","doi-asserted-by":"crossref","unstructured":"Wang X et al (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"},{"key":"9795_CR6","doi-asserted-by":"crossref","unstructured":"Zhang H, McAuley J (2020) Stacked mixed-order graph convolutional networks for collaborative filtering. In: Proceedings of the 2020 SIAM international conference on data mining, SIAM, pp 73\u201381","DOI":"10.1137\/1.9781611976236.9"},{"key":"9795_CR7","doi-asserted-by":"crossref","unstructured":"He X et al (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":"9795_CR8","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"9795_CR9","unstructured":"Gasteiger J, Bojchevski A, G\u00fcnnemann S (2018) Predict then propagate: Graph neural networks meet personalized pagerank. arXiv:1810.05997"},{"issue":"6","key":"9795_CR10","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TKDE.2017.2663422","volume":"29","author":"L Wu","year":"2017","unstructured":"Wu L, Ge Y et al (2017) Modeling the evolution of users\u2019 preferences and social links in social networking services. IEEE Trans Knowl Data Eng 29(6):1240\u20131253","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9795_CR11","doi-asserted-by":"crossref","unstructured":"Wang H, Zhao M, et al (2019) Knowledge graph convolutional networks for recommender systems. In: The world wide web conference, pp 3307\u20133313","DOI":"10.1145\/3308558.3313417"},{"key":"9795_CR12","doi-asserted-by":"crossref","unstructured":"Chen Y, Yang Y, Wang Y, Bai J, Song X, King I (2022) Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation. In: 2022 IEEE 38th international conference on data engineering (ICDE), IEEE, pp 299\u2013311","DOI":"10.1109\/ICDE53745.2022.00027"},{"key":"9795_CR13","doi-asserted-by":"crossref","unstructured":"Wang H, Zhang F, et al (2019) Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 968\u2013977","DOI":"10.1145\/3292500.3330836"},{"key":"9795_CR14","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Cao Y, Liu M, Chua T-S (2019) Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 950\u2013958","DOI":"10.1145\/3292500.3330989"},{"key":"9795_CR15","doi-asserted-by":"crossref","unstructured":"Wang Z, Lin G, Tan H, Chen Q, Liu X (2020) Ckan: collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, pp 219\u2013228","DOI":"10.1145\/3397271.3401141"},{"key":"9795_CR16","doi-asserted-by":"crossref","unstructured":"Wang X, Huang T, Wang D, Yuan Y, Liu Z, He X, Chua T-S (2021) Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the web conference 2021, pp 878\u2013887","DOI":"10.1145\/3442381.3450133"},{"key":"9795_CR17","doi-asserted-by":"crossref","unstructured":"Zou D, Wei W, Wang Z, Mao X-L, Zhu F, Fang R, Chen D (2022) Improving knowledge-aware recommendation with multi-level interactive contrastive learning. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 2817\u20132826","DOI":"10.1145\/3511808.3557358"},{"key":"9795_CR18","doi-asserted-by":"crossref","unstructured":"Yang Y, Huang C, Xia L, Li C (2022) Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1434\u20131443","DOI":"10.1145\/3477495.3532009"},{"key":"9795_CR19","doi-asserted-by":"crossref","unstructured":"Ying R et al (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"key":"9795_CR20","doi-asserted-by":"crossref","unstructured":"Zhang J, Shi X, Zhao S, King I (2019) Star-gcn: stacked and reconstructed graph convolutional networks for recommender systems. arXiv:1905.13129","DOI":"10.24963\/ijcai.2019\/592"},{"key":"9795_CR21","doi-asserted-by":"crossref","unstructured":"Sun J et al (2019) Multi-graph convolution collaborative filtering. In: 2019 IEEE international conference on data mining (ICDM), IEEE, pp 1306\u20131311","DOI":"10.1109\/ICDM.2019.00165"},{"key":"9795_CR22","doi-asserted-by":"publisher","first-page":"34433","DOI":"10.1109\/ACCESS.2021.3061915","volume":"9","author":"D Mei","year":"2021","unstructured":"Mei D, Huang N, Li X (2021) Light graph convolutional collaborative filtering with multi-aspect information. IEEE Access 9:34433\u201334441. https:\/\/doi.org\/10.1109\/ACCESS.2021.3061915","journal-title":"IEEE Access"},{"key":"9795_CR23","doi-asserted-by":"crossref","unstructured":"Wang X et al (2020) Disentangled graph collaborative filtering. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1001\u20131010","DOI":"10.1145\/3397271.3401137"},{"key":"9795_CR24","doi-asserted-by":"crossref","unstructured":"Wang X et al (2020) Multi-component graph convolutional collaborative filtering. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 6267\u20136274","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"9795_CR25","doi-asserted-by":"crossref","unstructured":"Yang Y, Wu L, Hong R, Zhang K, Wang M (2021) Enhanced graph learning for collaborative filtering via mutual information maximization. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 71\u201380","DOI":"10.1145\/3404835.3462928"},{"key":"9795_CR26","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1007\/s00607-022-01099-w","volume":"104","author":"H Huang","year":"2022","unstructured":"Huang H, Tian X, Luo S, Shi Y (2022) Item enhanced graph collaborative network for collaborative filtering recommendation. Computing 104:2541\u20132556","journal-title":"Computing"},{"key":"9795_CR27","doi-asserted-by":"crossref","unstructured":"Li G, Guo Z, Li J, Wang C (2022) Mdgcf: Multi-dependency graph collaborative filtering with neighborhood-and homogeneous-level dependencies. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 1094\u20131103","DOI":"10.1145\/3511808.3557390"},{"issue":"1","key":"9795_CR28","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TCSS.2022.3151822","volume":"10","author":"K Liu","year":"2022","unstructured":"Liu K, Xue F, He X, Guo D, Hong R (2022) Joint multi-grained popularity-aware graph convolution collaborative filtering for recommendation. IEEE Trans Comput Soc Syst 10(1):72\u201383","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"9795_CR29","doi-asserted-by":"crossref","unstructured":"Fan Z, Xu K, Dong Z, Peng H, Zhang J, Yu PS (2023) Graph collaborative signals denoising and augmentation for recommendation. In: Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pp 2037\u20132041","DOI":"10.1145\/3539618.3591994"},{"key":"9795_CR30","doi-asserted-by":"crossref","unstructured":"Chen L et al (2020) Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 4, pp 27\u201334","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"9795_CR31","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9795_CR32","doi-asserted-by":"publisher","first-page":"77407","DOI":"10.1109\/ACCESS.2021.3083600","volume":"9","author":"R Guo","year":"2021","unstructured":"Guo R et al (2021) A simple graph convolutional network with abundant interaction for collaborative filtering. IEEE Access 9:77407\u201377415. https:\/\/doi.org\/10.1109\/ACCESS.2021.3083600","journal-title":"IEEE Access"},{"key":"9795_CR33","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, PMLR, pp 1725\u20131735"},{"key":"9795_CR34","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z, Zhu L, Gao Z, Nie L (2021) Interest-aware message-passing gcn for recommendation. In: Proceedings of the web conference 2021, pp 1296\u20131305","DOI":"10.1145\/3442381.3449986"},{"key":"9795_CR35","doi-asserted-by":"crossref","unstructured":"Mao K et al (2021) Ultragcn: ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 1253\u20131262","DOI":"10.1145\/3459637.3482291"},{"key":"9795_CR36","doi-asserted-by":"crossref","unstructured":"Guo Z, Wang C, Li Z, Li J, Li G (2022) Joint locality preservation and adaptive combination for graph collaborative filtering. In: International conference on database systems for advanced applications, Springer, pp 183\u2013198","DOI":"10.1007\/978-3-031-00126-0_12"},{"key":"9795_CR37","doi-asserted-by":"crossref","unstructured":"He L et al (2023) Simplifying graph-based collaborative filtering for recommendation. In: Proceedings of the sixteenth ACM international conference on web search and data mining, pp 60\u201368","DOI":"10.1145\/3539597.3570451"},{"key":"9795_CR38","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"9795_CR39","unstructured":"Oono K, Suzuki T (2019) Graph neural networks exponentially lose expressive power for node classification. arXiv:1905.10947"},{"key":"9795_CR40","doi-asserted-by":"crossref","unstructured":"Li G et al (2019) Deepgcns: Can gcns go as deep as cnns? In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9267\u20139276","DOI":"10.1109\/ICCV.2019.00936"},{"key":"9795_CR41","doi-asserted-by":"crossref","unstructured":"Ji S, Feng Y, Ji R, Zhao X, Tang W, Gao Y (2020) Dual channel hypergraph collaborative filtering. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2020\u20132029","DOI":"10.1145\/3394486.3403253"},{"key":"9795_CR42","doi-asserted-by":"crossref","unstructured":"Kong T et al (2022) Linear, or non-linear, that is the question! In: Proceedings of the fifteenth ACM international conference on web search and data mining, pp 517\u2013525","DOI":"10.1145\/3488560.3498501"},{"key":"9795_CR43","unstructured":"Rendle S et al (2012) Bpr: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618"},{"key":"9795_CR44","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"issue":"3","key":"9795_CR45","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1007\/s00521-022-07735-y","volume":"35","author":"X Li","year":"2023","unstructured":"Li X et al (2023) Effective hybrid graph and hypergraph convolution network for collaborative filtering. Neural Comput Appl 35(3):2633\u20132646","journal-title":"Neural Comput Appl"},{"key":"9795_CR46","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Vbpr: visual bayesian personalized ranking from implicit feedback. In: Proceedings of the AAAI conference on artificial intelligence, vol 30","DOI":"10.1609\/aaai.v30i1.9973"},{"key":"9795_CR47","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th international conference on world wide web, pp 507\u2013517","DOI":"10.1145\/2872427.2883037"},{"issue":"8","key":"9795_CR48","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"9795_CR49","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"9795_CR50","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wang P, Zhao X, Qi H, He J, Jin J, Peng C, Lin Z, Shao J (2022) Ia-gcn: interactive graph convolutional network for recommendation. arXiv:2204.03827","DOI":"10.1145\/3583780.3615232"},{"key":"9795_CR51","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR workshop and conference proceedings, pp 249\u2013256."},{"issue":"2","key":"9795_CR52","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3233\/IDA-205725","volume":"26","author":"K Liu","year":"2022","unstructured":"Liu K, Xue F, Hong R (2022) Rgcf: Refined graph convolution collaborative filtering with concise and expressive embedding. Intell Data Anal 26(2):427\u2013445","journal-title":"Intell Data Anal"},{"key":"9795_CR53","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski J, Tassi CRN, Ali M, Lee J, Humt M, Feng J, Kruspe A, Triebel R, Jung P, Roscher R et al (2023) survey of uncertainty in deep neural networks. Art Intell Rev 56:1513\u20131589","journal-title":"Art Intell Rev"},{"key":"9795_CR54","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhu Y, Liu Q, Wu S, Wang S, Wang L (2021) Mining latent structures for multimedia recommendation. In: Proceedings of the 29th ACM international conference on multimedia, pp 3872\u20133880","DOI":"10.1145\/3474085.3475259"},{"issue":"1","key":"9795_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10705511.2022.2074422","volume":"30","author":"T Asparouhov","year":"2023","unstructured":"Asparouhov T, Muth\u00e9n B (2023) Residual structural equation models. Struct Equ Modeling 30(1):1\u201331","journal-title":"Struct Equ Modeling"},{"issue":"1","key":"9795_CR56","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.nanoms.2022.08.001","volume":"5","author":"C Guo","year":"2023","unstructured":"Guo C, Li G, Li S, Hu X, Lu H, Li X, Xu Z, Chen Y, Li Q, Lu J et al (2023) Additive manufacturing of ni-based superalloys: residual stress, mechanisms of crack formation and strategies for crack inhibition. Nano Mater Sci 5(1):53\u201377","journal-title":"Nano Mater Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09795-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09795-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09795-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T18:15:33Z","timestamp":1723227333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09795-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,2]]},"references-count":56,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["9795"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09795-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,2]]},"assertion":[{"value":"16 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2024","order":3,"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 we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}