P2P Lending recommender systems are embracing portraying schemes to obtain profiles of both loan and lender, and thus to overcome inherent limitations of general recommendation models. A successful recommendation method requires proper handling the interactions between loans and lenders. We argue that three fundamental problems need to be addressed: 1) how to fully utilize different properties of loan for establishing its profile, 2) how to adapt social and psychological factors for enhancing lender’s profile, and 3) how to exploit the interactions between loan and lender. To the best of our knowledge, there lacks a unified framework that addresses these problems.

In this work, we contribute a new solution named RRWP (Recommendation via Random Walk with Profile of loan and lender), for learning recommender systems for P2P Lending. We develop a hybrid graph random walk-based model to capture the complicated interactions between loans and lenders. In particular, the algorithm consists of three stages for better P2P lending recommendation. (1) Loan profile is built by utilizing attributes of both loan and borrower; (2) Lender profile is established via his social and psychological factors together with interactions between loan and lender; (3) A hybrid graph is constructed based on which random walk is performed to recommend for both loan and lender. Extensive experiments on real-world dataset demonstrate the effectiveness of RRWP. Further analysis reveals that profile modelling is consistent with the basic investment theory in finance.

P2P贷款推荐系统正在采用描绘方案来获取贷款和贷方的概况，从而克服了一般推荐模型的固有局限性。成功的推荐方法需要正确处理贷款和贷方之间的相互作用。我们认为需要解决三个基本问题：1）如何充分利用贷款的不同属性来建立其特征； 2）如何适应社会和心理因素来增强贷方的特征； 3）如何利用贷款之间的相互作用和贷方。据我们所知，缺少一个解决这些问题的统一框架。

在这项工作中，我们为学习P2P借贷推荐系统提供了一种名为RRWP（通过带有贷款和贷方资料的Random Walk推荐）的新解决方案。我们开发了一个基于混合图随机游动的模型来捕获贷款和贷方之间的复杂交互。特别地，该算法包括三个阶段，以实现更好的P2P贷款推荐。（1）贷款概况是通过利用借方和借方的属性来建立的；（2）借贷者的社会心理因素以及借贷者之间的互动关系建立借贷者形象；（3）构建混合图，基于该图进行随机游动以推荐贷款和贷方。在真实数据集上的大量实验证明了RRWP的有效性。进一步的分析表明，配置文件建模与金融中的基本投资理论是一致的。