A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users

TL;DR

This paper introduces a novel cross-domain insurance recommendation system using heterogeneous information networks to address cold start user challenges, leveraging advanced neural network techniques for improved accuracy.

Contribution

The paper proposes a new Heterogeneous information network based Cross Domain Insurance Recommendation (HCDIR) framework that effectively models complex insurance data and user interests for cold start scenarios.

Findings

HCDIR significantly outperforms existing methods on Jinguanjia dataset.

Utilizes multi-level attention mechanisms for richer user and product representations.

Employs GRU to model user dynamic interests in the source domain.

Abstract

Internet is changing the world, adapting to the trend of internet sales will bring revenue to traditional insurance companies. Online insurance is still in its early stages of development, where cold start problem (prospective customer) is one of the greatest challenges. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods could not be applied to insurance domain directly due to the domain specific properties. In this paper, we propose a novel framework called a Heterogeneous information network based Cross Domain Insurance Recommendation (HCDIR) system for cold start users. Specifically, we first try to learn more effective user and item latent features in both source and target domains. In source domain, we employ gated recurrent unit (GRU) to module user dynamic interests. In target domain, given the complexity of insurance products and the data sparsity problem, we construct an insurance heterogeneous information network (IHIN) based on data from PingAn Jinguanjia, the IHIN connects users, agents, insurance products and insurance product properties together, giving us richer information. Then we employ three-level (relational, node, and semantic) attention aggregations to get user and insurance product representations. After obtaining latent features of overlapping users, a feature mapping between the two domains is learned by multi-layer perceptron (MLP). We apply HCDIR on Jinguanjia dataset, and show HCDIR significantly outperforms the state-of-the-art solutions.