Investment Recommendation in P2P Lending: A Portfolio Perspective with Risk Management

Abstract

P2P lending is an online platform to make borrowing and investment transactions. A central question on these platforms is how to align the right products with the right investors, thus helping investors to make better decisions. Along this line, tremendous efforts have been devoted to modeling the credits of products and borrowers from an economic perspective. However, these global models are only exploratory in nature and are not practical. In this paper, we focus on the personalized investment recommendation by reconstructing the two steps for investment decision making: what to buy and how much money to pay. Specifically, we first generate a candidate investment recommendation list for each investor that tackles "what to buy" problem. In this process, we consider various unique properties of investment recommendation. Furthermore, according to the portfolio theory, we optimize the shares of each recommended candidate by incorporating the investments an investor currently holds, thus solving the "how much money to pay" problem. Finally, extensive experimental results on a large-scale real world dataset show the effectiveness of our model under various evaluation metrics.

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