The Research of Recommendation Algorithms in P2P Lending

Abstract

With the rapid development of P2P lending in China In recent years, how to recommend bid projects to the bidder to meet its preferences will become a research hotspot, but there are few studies in this area. And since the P2P lending platform is not a dominant scoring mechanism, it is an urgent need to solve the problem of how to generate a reasonable score matrix. First of all, from the PPDai.com to climb over more than 320 thousand tenders of the last two years. Then, based on the deeply analysis of the data characteristics of the P2P lending areas, three types of different types of scoring matrix (Boolean, continuous and mixed) are generated. Then, three kinds of classical collaborative filtering recommendation algorithms are used in the experiment. The results show that the performance of the recommendation algorithm has important influence on the performance of the recommendation algorithm, and the performance of cooperative filtering algorithms is different in P2P lending compared with in some other areas. Finally, the experimental results are analyzed, and the relevant suggestions are given for the P2P lending areas.