# 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. neural MF; recommender systems; loan recommendation +
Footnote †: journal: Applied  
Despite the fact that a great deal of studies have been conducted for cold start recommendations, none of them can handle all three challenges. To solve the first challenge, many context-based recommendation methods [6; 7] are proposed. These methods convert the statements to several categories to capture the semantics of statements. We argue that semantic information can be better modeled by semantic representations, yet these methods fail. For the second challenge, although many cold start recommendation models, such as JIM [8] and CTLM [9], are proposed for general recommendations, the problem's definitions in their works are quite different from ours. However, most of these works are designed for instant feedback applications and cannot be used for loan recommendations. Apart from these methods, previous credit scoring methods [10; 11; 12; 13] are also related to our problem but these methods cannot handle cold start problems. Moreover, for the third challenge, there is no existing work considering the mixture evaluation metrics. With the emergence of natural language processing techniques, the semantic of both applicant statements and the loan project characters can be represented in a unified hyperspace, which enables us to solve the challenges systematically. Unfortunately, most of existing loan recommendation techniques [3; 14; 15] follow collaborate filtering frameworks, which cannot be easily adapted to textual data. Even worse, recently proposed methods [16; 17] assume that the interactions of applicants are available and employ graph neural networks to model it. These methods are not suitable in our task because the applicant's interactions violate the cold start setting. In this paper, we propose a novel semantic-enhanced neural matrix factorization for cold start recommendations for loan, abbreviated as CSRLoan. Instead of modeling the user-specified preference, CSRLoan directly used the provided semantic information to build the recommender system. Loans that have similar statements in semantics would be recommended to the same applicants. In this way, both the applicants and the loans can be viewed as several latent groups. When a new loan comes, the model will first decide the group it belongs to and then recommends it to the right applicant. CSRLoan contains three key modules, i.e., statement encoding, dual NMF, and mixture learning target. To capture the semantic information of statements, CSRLoan employs a pre-training algorithm to learn the initial representations of used word embeddings and to conduct a transformer network to encode each statement into a representation vector.