Hybrid Insurance Recommendation Algorithm Integrating Deep Neural Networks and Knowledge Graphs Based on Matrix Factorization

Abstract

To address issues such as data sparsity, overfitting, and the inability to fully extract latent information in traditional recommendation methods, a new insurance product recommendation algorithm is proposed that combines knowledge graphs improved by matrix factorization with deep neural networks (DNN). First, to tackle the issue of sparse data in existing insurance products, an improved knowledge graph recommendation algorithm based on matrix factorization (FunkSVD) is proposed. By training the data through matrix factorization the problem of data sparsity is mitigated. After refining the customer-product matrix, knowledge graph triples are constructed based on customer characteristics and insurance product features such as insured age and coverage. Feature extraction and customer preference prediction are carried out using an alternating learning approach within a multi-task knowledge graph framework. Then, to alleviate issues like local optima and vanishing gradients during recommendation, DNN is applied for further recommendation. A fully connected layer is constructed, and forward propagation and backpropagation algorithms are used to train customer features and product matrices, predicting customer purchasing behavior and generating recommendations. Finally, comparative experimental results show that, compared to other recommendation algorithms such as collaborative filtering and DNN, the proposed algorithm improves accuracy, recall, F1 score, and other metrics. This algorithm not only speeds up recommendations but also improves recommendation quality.