TY - GEN
T1 - Investment Recommendation in P2P Lending
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
AU - Zhao, Hongke
AU - Wu, Le
AU - Liu, Qi
AU - Ge, Yong
AU - Chen, Enhong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Investment Recommendation
KW - P2P Lending
KW - Portfolio Perspective
UR - https://www.scopus.com/pages/publications/84936972909
UR - https://www.scopus.com/pages/publications/84936972909#tab=citedBy
U2 - 10.1109/ICDM.2014.104
DO - 10.1109/ICDM.2014.104
M3 - Conference contribution
AN - SCOPUS:84936972909
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1109
EP - 1114
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2014 through 17 December 2014
ER -