Mining social lending motivations for loan project recommendations
Mining social lending motivations for loan project recommendations
复制标题
挖掘社会借贷动机进行贷款项目推荐
DOI：
10.1016/j.eswa.2017.11.010
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发表时间：
2018-11-30
影响因子：
8.5
通讯作者：
Zhu, Hong
中科院分区：
文献类型：
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作者：
Yan, Jiaqi;Wang, Kaixin;Zhu, Hong
在线社交借贷促进了借款人向贷款人寻求融资支持的能力。随着社交借贷项目的不断增加，贷款人很难找到合适的项目进行投资，借款人也很难获得所需的资金。项目推荐技术在一定程度上为解决这一问题提供了一种很有希望的方法，它将借款人的项目推荐给有能力投资的贷款人。不幸的是，目前的贷款项目建议只探索了一些结构化的信息来匹配借款人和贷款人，因此他们不能达到一个令人满意的方式来很好地解决问题。在本研究中，我们创新性地挖掘了大量的非结构化数据，即借款人和贷款人动机的文本数据，以提供解决借款人和贷款人不匹配问题的贷款项目建议。我们提出了一种基于动机的推荐方法，该方法使用文本挖掘和分类器技术来识别借款人和贷款人的动机。使用知名社交借贷平台Kiva的数据集，我们的实验结果表明，与之前的工作相比，所提出的方法改善了不活跃贷款人群体和不受欢迎的贷款群体的项目推荐，这表明所提出的方法在解决贷款项目推荐中的数据稀疏性和冷启动问题方面具有优势。因此，本研究试图通过挖掘大量P2P平台中的大量非结构化文本数据，解决信息过载问题，改善借款人和贷款人之间的匹配。(C) 2017 Elsevier Ltd.版权所有。
Online social lending has facilitated the ability of borrowers to reach lenders for financing support. With the increasing number of social lending projects, it is becoming very difficult for lenders to find appropriate projects to invest in, and for borrowers to get the funds they need. Project recommendation techniques provide a promising way to solve this problem to some degree, by recommending borrowers' projects to lenders who are able to invest. Unfortunately, current loan project recommendations only explore some structured information to match borrowers and lenders, so they cannot achieve a satisfactory way to solve the problem very well. In this study, we innovatively mine a huge amount of unstructured data, the text data of borrowers' and lenders' motivations, to provide loan project recommendations that solve the problem of mismatches between borrowers and lenders. We present a motivation-based recommendation approach that uses text mining and classifier techniques to identify borrowers' and lenders' motivations. Using a dataset from the well-known social lending platform Kiva, our experiment results show that, compared with prior works, the proposed approach improves project recommendations in inactive lender groups and unpopular loan groups, which shows the superiority of the proposed approach in addressing data sparsity and cold start problems in loan project recommendations. This study thus initiates an attempt to solve the information overload problem and improve matching between borrowers and lenders through mining big unstructured text data found in a large number of P2P platforms. (C) 2017 Elsevier Ltd. All rights reserved.