Research On Personalized Recommendation Of P2P Lending Platform For Lender Investment

In order to effectively improve the investment efficiency of lenders and meet the demand of lenders for personalized investment,this thesis systematically proposes a personalized recommendation system,model and algorithm for the lenders of P2P lending platform to assist lenders in making investment decisions,the specific research content is as follows:This thesis firstly sorts out the historical related literatures: most scholars are studying the recommendation system architecture and technical problems from the theoretical level,and consider less specific scenarios of lender investment decisions on the P2P lending platform.In addition,traditional personalized recommendations The system,model and algorithm face multiple limitations in application:(1)The personalized recommendation of online loan products is actually the personalized recommendation research of investment,which is quite different from the personalized recommendation in other fields.(2)The complexity of lender's investment decision-making behavior and the particularity of online loan product attributes.Then,the P2P lending platform and its personalized recommendation status are studied,and the problems of platform personalization function and data availability and integrity are poor,and the platform pays less attention to potential users.The traditional manual recommendation method is considered to be mechanized and accurate.The high and low efficiency,the platform's existing service methods can not meet the strong demand of personalized customers;so from the platform's operation drive and the lender's decision-driven two aspects to determine the P2P lending platform personalized recommendation for lenders Specific needs scenario.Subsequently,a personalized recommendation system and model for P2P lender investment were proposed.Based on the generalized recommendation framework and model,this paper combines the actual scenarios of lender investment and improves from three aspects: combining portfolio optimization strategy module,introducing dynamic feedback mechanism,increasing P2P lending platform participation,and based on The idea elaborates on the improved system and model.Finally,this paper summarizes the limitations of traditional algorithms applied to P2P lending platform,and uses collaborative filtering as the basis algorithm,combined with the specific investment needs of lenders,proposes personalizedization for P2P lending platform lenders' investment decision-making scenarios.The recommendation algorithm is given.An example of lender investment is given.The specific steps of the algorithm are simulated and the results are evaluated.The validity and feasibility of the algorithm are verified.