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

Liu, Yuhang; Ma, Huifang; Jiang, Yanbin; Li, Zhixin

doi:10.1007/s10489-021-02680-0

Cited by 7 publications

(8 citation statements)

References 38 publications

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“…(14) Step 14: Vensim dynamically calculates the current value of each level variable based on the values of the auxiliary variables and the model ( 27)-( 44). (15) Step 15: Simulate a drawing of the online lending platform. (16) Step 16: If t < T, then t � t + λ; return to Step 8.…”

Section: (40)mentioning

confidence: 99%

“…In addition, repayment term, description, and number of failed loans had a negative impact on loan success; finally, it was found that men were more likely to obtain loans. Liu et al [ 15 ] proposed a P2P lending recommendation (P2PLR) system, which pairs borrowers and investors according to their historical interaction information and identifies investors' preferences for risk and return awareness by establishing risk and return awareness models to improve lending efficiency. Chen et al [ 16 ] analyzed the data of some online lending platforms in China from June 2017 to 2018 and believed that the interest rate of online lending platforms in China was not fully marketized, which was mainly determined by the platforms themselves and was also related to the loan term, number of new investors, repayment amount, and platform credit rating.…”

Section: Introductionmentioning

confidence: 99%

“…Formula (15) shows that the increase rate of actual investors is the minimum value between the total investable amount and the expected loan amount, which is divided by the average investment amount.…”

mentioning

confidence: 99%

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System Dynamics Models of Online Lending Platform Based on Vensim Simulation Technology and Analysis of Interest Rate Evolution Trend

Pang

Deng

Chen

2022

Computational Intelligence and Neuroscience

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This paper mainly focuses on the construction of a system dynamics model of an online lending platform and the application of Vensim simulation technology in the analysis of the evolution trend of the investment interest rate. The system dynamics flow graph models of the investment subsystem, loan subsystem, and interest rate subsystem of the online lending platform were innovatively constructed; the level variable, flow rate variable, auxiliary variable, and mathematical relations between these variables as they relate to the platform were innovatively studied. Based on the key variables of the online lending platform, a system dynamics model of the online lending platform was innovatively constructed. The parameters were assigned using data from China’s online lending industry, and the logical consistency, sensitivity, and validity of the simulation data of the system dynamics model were tested. Finally, we used Vensim simulation technology to simulate and analyze the impact of emergency scenarios on the interest rate evolution trend. Overall, this paper provides a scientific simulation technology and data analysis method for examining online lending platforms.

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Section: (40)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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System Dynamics Models of Online Lending Platform Based on Vensim Simulation Technology and Analysis of Interest Rate Evolution Trend

Pang

Deng

Chen

2022

Computational Intelligence and Neuroscience

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“…The last decades have witnessed the flourishing of the World Wide Web, facilitating the development of online recommender systems (a.k.a., recommendation). Recommender systems have become essential components for internet applications (i.e., micro-video [ 1 ], E-commerce [ 2 ], and P2P lending [ 3 ]) to discover latent user interests and select items of interest for users accurately and in a timely manner based on a user-item historical interaction network. To alleviate the inherent sparsity and cold-start problems of recommender systems, an increasing amount of cutting-edge research has focused on recommendation methods that incorporate auxiliary information to capture deeper features and improve recommendation performance, including social networks [ 4 – 6 ], tags [ 7 ], and multi-modal information [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks

Zhang

et al. 2022

Appl Intell

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To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user interests and preferences. Knowledge Graphs (KGs), consisting of real-world objective facts and fruitful entities, play a vital role in recommender systems. Recently, a technological trend has been to develop end-to-end Graph Neural Networks (GNNs)-based knowledge-aware recommendation (a.k.a., Knowledge Graph Recommendation, KGR) models. Unfortunately, current GNNs-based KGR approaches focus on how to capture high-order feature information on KGs while neglecting the following two crucial limitations: 1) The explicitly high-order feature interaction and fusion mechanism and 2) The valid user intent modelling mechanism. As such, these issues lead to insufficient user/item representation learning capability and unsatisfactory KGR performance. In this work, we present a novel Knowledge-enhanced Re commendation with F eature I nteraction and Intent-aware Attention Networks (FIRE) to address the latent intent modelling and high-order feature interaction deficiencies ignored by existing KGR methods. Based on the prototype user/item representation learning leveraging the GNNs-based approach, our model offers the following major improvements: One is the innovative use of Convolutional Neural Networks (CNNs) that perform vertical convolutional (a.k.a., bit-level convolutional) and horizontal convolutional (a.k.a., vector-level convolutional) processes to model multi-granular high-order feature interactions to enhance item-side representation learning. Another is to model users’ latent intent factors by utilizing a two-level attention mechanism (i.e., node- and intent-level attention mechanism) to enhance user-side representation learning. Extensive experiments on three KGs domain public datasets demonstrate that our method outperforms the existing state-of-the-art baseline. Last but not least, numerous ablation- and model studies demystify the working mechanism and elucidate the plausibility of the proposed model.

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“…Unfortunately, most of existing loan recommendation techniques [3,14,15] follow collaborate filtering frameworks, which cannot be easily adapted to textual data. Even worse, recently proposed methods [16,17] assume that the interactions of applicants are available and employ graph neural networks to model it. These methods are not suitable in our task because the applicant's interactions violate the cold start setting.…”

Section: Introductionmentioning

confidence: 99%

CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization

Zhuang¹,

Wu²,

Liu³

2022

Applied Sciences

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Recommending loan products to applicants would benefit many financial businesses and individuals. Nevertheless, many loan products suffer from the cold start problem; i.e., there are no available historical data for training the recommendation model. Considering the delayed feedback and the complex semantic properties of loans, methods for general cold start recommendation cannot be directly used. Moreover, existing loan recommendation methods ignore the default risk, which should be evaluated along with the approval rate. To solve these challenges, we propose CSRLoan for cold start loan recommendation. CSRLoan employs pretraining techniques to learn the embeddings of statements, which captures the intrinsic semantic information of different loans. For recommendation, we design a dual neural matrix factorization(NMF) model, which can not only capture the semantic information of both loan products and applicants but also generate the recommendation results and default risk at the same time. Extensive experiments have been conducted on real-world datasets to evaluate the effectiveness and efficiency of the proposed CSRLoan.

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

Cited by 7 publications

References 38 publications

System Dynamics Models of Online Lending Platform Based on Vensim Simulation Technology and Analysis of Interest Rate Evolution Trend

System Dynamics Models of Online Lending Platform Based on Vensim Simulation Technology and Analysis of Interest Rate Evolution Trend

FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks

CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization

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