{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T10:01:00Z","timestamp":1767866460658,"version":"3.49.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s10489-021-02680-0","type":"journal-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T04:14:59Z","timestamp":1627704899000},"page":"4999-5014","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Modelling risk and return awareness for p2p lending recommendation with graph convolutional networks"],"prefix":"10.1007","volume":"52","author":[{"given":"Yuhang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5104-8982","authenticated-orcid":false,"given":"Huifang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"2680_CR1","doi-asserted-by":"crossref","unstructured":"Luo C, Xiong H, Zhou W, Guo Y, Deng G (2011) Enhancing investment decisions in P2P lending: an investor composition perspective. In: Apt\u00e9 C, Ghosh J, Smyth P (eds) Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, San Diego, pp 292\u2013300","DOI":"10.1145\/2020408.2020458"},{"key":"2680_CR2","doi-asserted-by":"crossref","unstructured":"Zhao H, Wu L, Liu Q, Ge Y, Chen E (2014) Investment recommendation in P2P lending: a portfolio perspective with risk management. In: Kumar R, Toivonen H, Pei J, Huang J Z, Wu X (eds) 2014 IEEE international conference on data mining, ICDM 2014. IEEE Computer Society, Shenzhen, pp 1109\u2013 1114","DOI":"10.1109\/ICDM.2014.104"},{"key":"2680_CR3","unstructured":"Gao H, Qin X, Barroso RJD, Hussain W, Xu Y, Yin Y (2020) Collaborative learning-based industrial IOT API recommendation for software-defined devices: the implicit knowledge discovery perspective. IEEE Transactions on Emerging Topics in Computational Intelligence"},{"issue":"2","key":"2680_CR4","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s11036-019-01246-2","volume":"25","author":"X Yang","year":"2020","unstructured":"Yang X, Zhou S, Cao M (2020) An approach to alleviate the sparsity problem of hybrid collaborative filtering based recommendations: the product-attribute perspective from user reviews. Mob Netw Appl 25(2):376\u2013390. https:\/\/doi.org\/10.1007\/s11036-019-01246-2","journal-title":"Mob Netw Appl"},{"key":"2680_CR5","doi-asserted-by":"crossref","unstructured":"Xu Y, Zhang H, Gao H, Song S, Yin Y, Hei L, Ding Y, Barroso RJD (2021) Preference discovery from wireless social media data in apis recommendation. Wirel Netw:1\u201311","DOI":"10.1007\/s11276-021-02543-z"},{"issue":"1","key":"2680_CR6","doi-asserted-by":"publisher","first-page":"6:1","DOI":"10.1145\/3391198","volume":"21","author":"H Gao","year":"2021","unstructured":"Gao H, Huang W, Duan Y (2021) The cloud-edge-based dynamic reconfiguration to service workflow for mobile ecommerce environments: A qos prediction perspective. ACM Trans Internet Techn 21 (1):6:1\u20136:23. https:\/\/doi.org\/10.1145\/3391198","journal-title":"ACM Trans Internet Techn"},{"issue":"4","key":"2680_CR7","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1007\/s11036-020-01535-1","volume":"25","author":"H Gao","year":"2020","unstructured":"Gao H, Kuang L, Yin Y, Guo B, Dou K (2020) Mining consuming behaviors with temporal evolution for personalized recommendation in mobile marketing apps. Mob Netw Appl 25(4):1233\u20131248. https:\/\/doi.org\/10.1007\/s11036-020-01535-1","journal-title":"Mob Netw Appl"},{"issue":"4","key":"2680_CR8","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1109\/TCCN.2020.3027681","volume":"6","author":"Y Yin","year":"2020","unstructured":"Yin Y, Cao Z, Xu Y, Gao H, Li R, Mai Z (2020) Qos prediction for service recommendation with features learning in mobile edge computing environment. IEEE Trans Cogn Commun Netw 6 (4):1136\u20131145. https:\/\/doi.org\/10.1109\/TCCN.2020.3027681","journal-title":"IEEE Trans Cogn Commun Netw"},{"key":"2680_CR9","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla J, Ortega F, Hernando A, Guti\u00e9rrez A (2013) Recommender systems survey. Knowl Based Syst 46:109\u2013132. https:\/\/doi.org\/10.1016\/j.knosys.2013.03.012","journal-title":"Knowl Based Syst"},{"issue":"1","key":"2680_CR10","doi-asserted-by":"publisher","first-page":"776","DOI":"10.2991\/ijcis.2017.10.1.52","volume":"10","author":"RY Toledo","year":"2017","unstructured":"Toledo RY, Mart\u00ednez L (2017) Fuzzy tools in recommender systems: A survey. Int J Comput Intell Syst 10(1):776\u2013803. https:\/\/doi.org\/10.2991\/ijcis.2017.10.1.52","journal-title":"Int J Comput Intell Syst"},{"key":"2680_CR11","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","volume":"74","author":"J Lu","year":"2015","unstructured":"Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: A survey. Decis Support Syst 74:12\u201332. https:\/\/doi.org\/10.1016\/j.dss.2015.03.008","journal-title":"Decis Support Syst"},{"key":"2680_CR12","doi-asserted-by":"publisher","unstructured":"Dong G, Lai KK, Yen J (2010) Credit scorecard based on logistic regression with random coefficients. In: Sloot PMA, van Albada GD, Dongarra JJ (eds) Proceedings of the international conference on computational science, ICCS 2010. Procedia Computer Science, University of Amsterdam. [Online]. Available: https:\/\/doi.org\/10.1016\/j.procs.2010.04.278, vol 1, no. 1. Elsevier, The Netherlands, pp 2463\u20132468","DOI":"10.1016\/j.procs.2010.04.278"},{"issue":"6","key":"2680_CR13","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/TFUZZ.2005.859320","volume":"13","author":"Y Wang","year":"2005","unstructured":"Wang Y, Wang S, Lai KK (2005) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820\u2013831. https:\/\/doi.org\/10.1109\/TFUZZ.2005.859320","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2680_CR14","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.ins.2017.12.017","volume":"432","author":"H Zhang","year":"2018","unstructured":"Zhang H, Zhao H, Liu Q, Xu T, Chen E, Huang X (2018) Finding potential lenders in P2P lending: A hybrid random walk approach. Inf Sci 432:376\u2013391","journal-title":"Inf Sci"},{"key":"2680_CR15","doi-asserted-by":"publisher","first-page":"114763","DOI":"10.1016\/j.eswa.2021.114763","volume":"174","author":"Y Liu","year":"2021","unstructured":"Liu Y, Ma H, Jiang Y, Li Z (2021) Learning to recommend via random walk with profile of loan and lender in p2p lending. Expert Syst Appl 174:114763","journal-title":"Expert Syst Appl"},{"key":"2680_CR16","doi-asserted-by":"crossref","unstructured":"Chai Y, Cong Y, Bai L, Cui L (2019) Loan recommendation in P2P lending investment networks: a hybrid graph convolution approach. In: 2019 IEEE international conference on industrial engineering and engineering management, IEEM 2019. IEEE, Macao, pp 945\u2013949","DOI":"10.1109\/IEEM44572.2019.8978499"},{"key":"2680_CR17","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Piwowarski B, Chevalier M, Gaussier E, Maarek Y, Nie J-Y, Scholer F (eds) Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, SIGIR 2019. ACM, Paris, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"2680_CR18","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y-D, Wang M (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Huang J, Chang Y, Cheng X, Kamps J, Murdock V, Wen J-R, Liu Y (eds) Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, SIGIR 2020. ACM, Virtual Event, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"2680_CR19","doi-asserted-by":"crossref","unstructured":"Su Y, Zhang R, Erfani SM, Gan J (2021) Neural graph matching based collaborative filtering. CoRR arXiv:https:\/\/arxiv.org\/abs\/2105.04067","DOI":"10.1145\/3404835.3462833"},{"key":"2680_CR20","doi-asserted-by":"publisher","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: Liu L, White RW, Mantrach A, Silvestri F, McAuley JJ, Baeza-Yates R, Zia L (eds) The World Wide Web Conference, WWW 2019. [Online]. Available: https:\/\/doi.org\/10.1145\/3308558.3313562. ACM, San Francisco, pp 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"2680_CR21","unstructured":"Zibriczky D (2016) Recommender systems meet finance: a literature review. In: Semeraro G, Musto C, Bauer M (eds) Proceedings of the 2nd international workshop on personalization & recommender systems in financial services, CEUR workshop proceedings, vol 1606. CEUR-WS.org, Bari, pp 3\u201310"},{"issue":"6","key":"2680_CR22","doi-asserted-by":"publisher","first-page":"72:1","DOI":"10.1145\/3078848","volume":"8","author":"H Zhao","year":"2017","unstructured":"Zhao H, Ge Y, Liu Q, Wang G, Chen E, Zhang H (2017) P2P lending survey: Platforms, recent advances and prospects. ACM Trans Intell Syst Technol 8(6):72:1\u201372:28","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2680_CR23","doi-asserted-by":"crossref","unstructured":"Lee EL, Lou J-K, Chen W-M, Chen Y-C, Lin S-D, Chiang Y-S, Chen K-T (2015) Toward effective yet impartial altruism: a fairness-aware loan recommender system for microfinance services","DOI":"10.1145\/2639968.2640064"},{"key":"2680_CR24","doi-asserted-by":"crossref","unstructured":"Zhao H, Liu Q, Wang G, Ge Y, Chen E (2016) Portfolio selections in P2P lending: A multi-objective perspective. In: Krishnapuram B, Shah M, Smola AJ, Aggarwal CC, Shen D, Rastogi R (eds) Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, San Francisco, pp 2075\u20132084","DOI":"10.1145\/2939672.2939861"},{"key":"2680_CR25","doi-asserted-by":"crossref","unstructured":"Ren K, Malik A (2019) Recommendation engine for lower interest borrowing on peer to peer lending (P2PL) platform. In: Barnaghi P M, Gottlob G, Manolopoulos Y, Tzouramanis T, Vakali A (eds) 2019 IEEE\/WIC\/ACM international conference on web intelligence, WI 2019. ACM, Thessaloniki, pp 265\u2013269","DOI":"10.1145\/3350546.3352528"},{"issue":"12","key":"2680_CR26","first-page":"76","volume":"3","author":"Y Ding","year":"2020","unstructured":"Ding Y, Cheng L, Jiang C (2020) Research on p2p network loan investment decision based on improved bipartite graph. Data Anal Knowl Discov 3(12):76\u201383","journal-title":"Data Anal Knowl Discov"},{"key":"2680_CR27","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1016\/j.ins.2020.09.067","volume":"546","author":"J Zhou","year":"2021","unstructured":"Zhou J, Zhang Q, Li X (2021) Fuzzy factorization machine. Inf Sci 546:1135\u20131147","journal-title":"Inf Sci"},{"key":"2680_CR28","doi-asserted-by":"publisher","first-page":"4656","DOI":"10.1109\/TSP.2020.3015046","volume":"68","author":"X Mestre","year":"2020","unstructured":"Mestre X, Vallet P (2020) On the resolution probability of conditional and unconditional maximum likelihood doa estimation. IEEE Trans Signal Process 68:4656\u20134671","journal-title":"IEEE Trans Signal Process"},{"issue":"9-10","key":"2680_CR29","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1016\/j.spl.2010.01.015","volume":"80","author":"A Gupta","year":"2010","unstructured":"Gupta A, Mehrotra KG, Mohan C (2010) A clustering-based discretization for supervised learning. Stat Probab Lett 80(9-10): 816\u2013824","journal-title":"Stat Probab Lett"},{"key":"2680_CR30","doi-asserted-by":"crossref","unstructured":"Jiang S-, Li X, Zheng Q, Wang L- (2009) Approximate equal frequency discretization method. In: 2009 WRI global congress on intelligent systems, vol 3, IEEE, pp 514\u2013518","DOI":"10.1109\/GCIS.2009.131"},{"issue":"8","key":"2680_CR31","first-page":"58","volume":"46","author":"Y Liu","year":"2020","unstructured":"Liu Y, Ma H, Liu H, Yu L (2020) An overlapping subspace k-means clustering algorithm. Comput Eng 46(8):58\u201363","journal-title":"Comput Eng"},{"key":"2680_CR32","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017, conference track proceedings, Toulon. OpenReview.net"},{"key":"2680_CR33","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: McIlraith S A, Weinberger K Q (eds) Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative Applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18). AAAI Press, New Orleans, pp 3538\u20133545","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"2680_CR34","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Guyon I, von Luxburg U, Bengio S, Wallach H M, Fergus R, Vishwanathan S V N, Garnett R (eds) Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, Long Beach, pp 1024\u20131034"},{"key":"#cr-split#-2680_CR35.1","doi-asserted-by":"crossref","unstructured":"Zhu H, Feng F, He X, Wang X, Li Y, Zheng K, Zhang Y (2020) Bilinear graph neural network with neighbor interactions. In: Bessiere C","DOI":"10.24963\/ijcai.2020\/202"},{"key":"#cr-split#-2680_CR35.2","unstructured":"(ed) Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI 2020. ijcai.org, pp 1452-1458"},{"key":"2680_CR36","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks"},{"key":"2680_CR37","doi-asserted-by":"crossref","unstructured":"Rendle S (2010) Factorization machines. In: Webb G I, Liu B, Zhang C, Gunopulos D, Wu X (eds) ICDM 2010, the 10th IEEE international conference on data mining. IEEE Computer Society, Sydney, pp 995\u20131000","DOI":"10.1109\/ICDM.2010.127"},{"key":"2680_CR38","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: bayesian personalized ranking from implicit feedback. In: Bilmes J A, Ng A Y (eds) UAI 2009, proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, Montreal, pp 452\u2013461"},{"key":"2680_CR39","doi-asserted-by":"crossref","unstructured":"Pasricha R, McAuley JJ (2018) Translation-based factorization machines for sequential recommendation. In: Pera S, Ekstrand M D, Amatriain X, O\u2019Donovan J (eds) Proceedings of the 12th ACM conference on recommender systems, RecSys 2018. ACM, Vancouver, pp 63\u201371","DOI":"10.1145\/3240323.3240356"},{"key":"2680_CR40","doi-asserted-by":"crossref","unstructured":"Zheng Y, Gao C, He X, Li Y, Jin D (2020) Price-aware recommendation with graph convolutional networks. In: 36th IEEE international conference on data engineering, ICDE 2020. IEEE, Dallas, pp 133\u2013144","DOI":"10.1109\/ICDE48307.2020.00019"},{"issue":"12","key":"2680_CR41","first-page":"2708","volume":"53","author":"Z Mengying","year":"2016","unstructured":"Mengying Z, Xiaolin Z, Chaohui W (2016) Investment recommendation based on risk and surplus in p2p lending. J Comput Res Dev 53(12):2708","journal-title":"J Comput Res Dev"},{"issue":"1","key":"2680_CR42","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TSMC.2017.2665038","volume":"48","author":"H Zhao","year":"2018","unstructured":"Zhao H, Liu Q, Zhu H, Ge Y, Chen E, Zhu Y, Du J (2018) A sequential approach to market state modeling and analysis in online P2P lending. IEEE Trans Syst Man Cybern Syst 48(1):21\u201333","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"2680_CR43","doi-asserted-by":"crossref","unstructured":"Nikolakopoulos AN, Karypis G (2019) Recwalk: Nearly uncoupled random walks for top-n recommendation. In: Culpepper JS, Moffat A, Bennett PN, Lerman K (eds) Proceedings of the twelfth ACM international conference on web search and data mining, WSDM 2019. ACM, Melbourne, pp 150\u2013158","DOI":"10.1145\/3289600.3291016"},{"key":"2680_CR44","doi-asserted-by":"crossref","unstructured":"Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Champin P-A, Gandon FL, Lalmas M, Ipeirotis PG (eds) Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018. ACM, Lyon, pp 689\u2013698","DOI":"10.1145\/3178876.3186150"},{"key":"2680_CR45","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Barrett R, Cummings R, Agichtein E, Gabrilovich E (eds) Proceedings of the 26th international conference on World Wide Web, WWW 2017. ACM, Perth, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"2680_CR46","doi-asserted-by":"crossref","unstructured":"Huang X, Song Q, Li Y, Hu X (2019) Graph recurrent networks with attributed random walks. In: Teredesai A, Kumar V, Li Y, Rosales R, Terzi E, Karypis G (eds) Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2019. ACM, Anchorage, pp 732\u2013 740","DOI":"10.1145\/3292500.3330941"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02680-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02680-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02680-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:18:32Z","timestamp":1672964312000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02680-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,31]]},"references-count":47,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["2680"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02680-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,31]]},"assertion":[{"value":"22 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}