Skip to main content
SpringerLink
Log in

Modelling risk and return awareness for p2p lending recommendation with graph convolutional networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

P2P lending recommendation (P2PLR) has become an important and popular component of P2P lending platform, which aim to align the right loans with the right investors according to historical interaction. Most of the existing P2PLR models take risk and return as side information into P2PLR to capture the relationship among loans. Although some of them have been proven effective, the following two insights are often neglected. First, risk, the possibility of investors’ loss, is the most important attribute in P2P lending platform, and the implicit capture of risk may lose critical information in the P2PLR scenario. Second, the preference and sensitivity of return on loans is unknown, which is only implicitly reflected in the loans that the investor has invested. These insights motivate us to propose a novel P2PLR model riSk and return Aware Recommendation (STAR), which propagates the influence of risk and return on investor to make the learned investor representations be risk and return aware. Particularly, we specify two STAR methods, named STHCW (riSk and return aware Recommendation with High-order Connectivity with Weight) and STHC (riSk and return aware Recommendation with High-order Connectivity without weight), based on two strategies of embedding and propagation mechanisms, respectively. We conduct extensive experiments on three real-world datasets, demonstrating the effectiveness of our proposed method in recommendation via risk and return. Further analysis reveals that modeling the risk and return awareness is particularly useful for learning the risk and return aware preference of investor.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. http://www.prosper.com/tools/DataExport.aspx

  2. https://www.ppdai.com/

References

  1. Luo C, Xiong H, Zhou W, Guo Y, Deng G (2011) Enhancing investment decisions in P2P lending: an investor composition perspective. In: Apté C, Ghosh J, Smyth P (eds) Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, San Diego, pp 292–300

  2. 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– 1114

  3. 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

  4. 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):376–390. https://doi.org/10.1007/s11036-019-01246-2

    Article  Google Scholar 

  5. 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:1–11

  6. 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):6:1–6:23. https://doi.org/10.1145/3391198

    Article  Google Scholar 

  7. 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):1233–1248. https://doi.org/10.1007/s11036-020-01535-1

    Article  Google Scholar 

  8. 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):1136–1145. https://doi.org/10.1109/TCCN.2020.3027681

    Article  Google Scholar 

  9. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132. https://doi.org/10.1016/j.knosys.2013.03.012

    Article  Google Scholar 

  10. Toledo RY, Martínez L (2017) Fuzzy tools in recommender systems: A survey. Int J Comput Intell Syst 10(1):776–803. https://doi.org/10.2991/ijcis.2017.10.1.52

    Article  Google Scholar 

  11. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: A survey. Decis Support Syst 74:12–32. https://doi.org/10.1016/j.dss.2015.03.008

    Article  Google Scholar 

  12. 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 2463–2468

  13. Wang Y, Wang S, Lai KK (2005) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820–831. https://doi.org/10.1109/TFUZZ.2005.859320

    Article  Google Scholar 

  14. 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 432:376–391

    Article  MathSciNet  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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 945–949

  17. 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 165–174

  18. 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 639–648

  19. Su Y, Zhang R, Erfani SM, Gan J (2021) Neural graph matching based collaborative filtering. CoRR arXiv:https://arxiv.org/abs/2105.04067

  20. 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 2022–2032

  21. 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 3–10

  22. 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):72:1–72:28

    Article  Google Scholar 

  23. 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

  24. 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 2075–2084

  25. 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 265–269

  26. 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):76–83

    Google Scholar 

  27. Zhou J, Zhang Q, Li X (2021) Fuzzy factorization machine. Inf Sci 546:1135–1147

    Article  MathSciNet  Google Scholar 

  28. Mestre X, Vallet P (2020) On the resolution probability of conditional and unconditional maximum likelihood doa estimation. IEEE Trans Signal Process 68:4656–4671

    Article  MathSciNet  Google Scholar 

  29. Gupta A, Mehrotra KG, Mohan C (2010) A clustering-based discretization for supervised learning. Stat Probab Lett 80(9-10): 816–824

    Article  MathSciNet  Google Scholar 

  30. 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 514–518

  31. Liu Y, Ma H, Liu H, Yu L (2020) An overlapping subspace k-means clustering algorithm. Comput Eng 46(8):58–63

    Google Scholar 

  32. 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

  33. 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 3538–3545

  34. 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 1024–1034

  35. 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 (ed) Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI 2020. ijcai.org, pp 1452–1458

  36. Velickovic P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks

  37. 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 995–1000

  38. 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 452–461

  39. Pasricha R, McAuley JJ (2018) Translation-based factorization machines for sequential recommendation. In: Pera S, Ekstrand M D, Amatriain X, O’Donovan J (eds) Proceedings of the 12th ACM conference on recommender systems, RecSys 2018. ACM, Vancouver, pp 63–71

  40. 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 133–144

  41. Mengying Z, Xiaolin Z, Chaohui W (2016) Investment recommendation based on risk and surplus in p2p lending. J Comput Res Dev 53(12):2708

    Google Scholar 

  42. 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):21–33

    Article  Google Scholar 

  43. 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 150–158

  44. 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 689–698

  45. 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 173–182

  46. 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– 740

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61762078, 61363058, 61966004), Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (MIMS18-08), Northwest Normal University young teachers research capacity promotion plan (NWNU-LKQN2019-2), Research Fund of Guangxi Key Laboratory of Trusted Software (kx202003) and Graduate Research Fund Project of Northwest Normal University (2019KYZZ012073).

Author information

Authors and Affiliations

  1. College of Computer science and engineering, Northwest Normal University, Lanzhou, Gansu, 730070, China

    Yuhang Liu, Huifang Ma & Yanbin Jiang

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, Guangxi, 541004, China

    Huifang Ma

  3. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi, 541004, China

    Huifang Ma & Zhixin Li

Authors
  1. Yuhang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huifang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanbin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhixin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huifang Ma.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Ma, H., Jiang, Y. et al. Modelling risk and return awareness for p2p lending recommendation with graph convolutional networks. Appl Intell 52, 4999–5014 (2022). https://doi.org/10.1007/s10489-021-02680-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02680-0

Keywords

Search

Navigation