CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization

Abstract

Recommending loan products to applicants would benefit many financial businesses and individuals. Nevertheless, many loan products suffer from the cold start problem; i.e., there are no available historical data for training the recommendation model. Considering the delayed feedback and the complex semantic properties of loans, methods for general cold start recommendation cannot be directly used. Moreover, existing loan recommendation methods ignore the default risk, which should be evaluated along with the approval rate. To solve these challenges, we propose CSRLoan for cold start loan recommendation. CSRLoan employs pretraining techniques to learn the embeddings of statements, which captures the intrinsic semantic information of different loans. For recommendation, we design a dual neural matrix factorization (NMF) model, which can not only capture the semantic information of both loan products and applicants but also generate the recommendation results and default risk at the same time. Extensive experiments have been conducted on real-world datasets to evaluate the effectiveness and efficiency of the proposed CSRLoan.