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

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 couldn't be applied to insurance domain directly due to the domain's 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 users' 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. CCS CONCEPTS • Applied computing → Online insurance; • Information systems → Data mining; • Networks → Data path algorithms.

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

“…As shown in Table 1, we categorize many mainstream HGNN models, which could be applied in many scenarios, e.g, link prediction [17,34] and recommendation [1,12,26]. Through analyzing the underlying graph data and the aggregation procedure of existing HGNNs, we propose a unified framework of HGNN that consists of three main components:…”

“…Taking recommender system as an example, it can be regarded as a bipartite graph consisting of users and items, and a lot of auxiliary information also has a complex network structure, which can be naturally modeled as a heterogeneous graph. Besides, some works [1,5,8,12,15,26,30,32] have achieved SOTA performance by designing heterogeneous graph neural network (HGNN). In fact, HGNNs can utilize the complex structure and rich semantic information [44], and have been widely applied in many fields, such as e-commerce [25,54], and security [20,39].…”

“…The reason is that previous evaluations are conducted from the modellevel sight, and we cannot accurately know the importance of each component due to diverse architecture designs and applied scenarios. To evaluate them from the sight of module-level, we first propose a unified framework of existing HGNNs that consists of Table 1: Different perspectives to categorize HGNN models: (1) The first row from the perspective of neighbors to be aggregated is mentioned in Section 2.1. (2) The second row shows we roughly divide the existing models into three categories mentioned in Section 4.…”

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

“…It extended stacked denoising autoencoders to effectively fuse review text and item contents with the rating matrix to generate user and item representations with more semantic information. Bi et al [20,21] proposed to construct a heterogeneous information network and took into consideration the interaction sequence information to learn effective user/item representations in each domain. The proposed approaches are proved to be effective in the cross-domain insurance recommendation.…”

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

“…These representations are considered the auxiliary information for the cold start recommendation. Adapting matrix factorization approaches can be used for cross-domain recommendation in such a way that aggregated information of user preferences from both domains are combined, however matrix factorization approaches have mostly been used in knowledge transfer and linkage approaches [38,39] for cross domain recommendation by leveraging the relatively richer information such as ratings from the source domain for improving cold start recommendation accuracy in the target domain. Therefore, cold start recommendation accuracy highly depends on the mapping accuracy of the latent factors across the source and target domains.…”