DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain

Abstract

Internet insurance products are apparently different from traditional e-commerce goods for their complexity, low purchasing frequency, etc. So, cold start problem is even worse. 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 into insurance domain directly due to product complexity. In this paper, we propose a Deep Cross-Domain Insurance Recommendation System (DCDIR) for cold start users. Specifically, we first learn more effective user and item latent features in both domains. In target domain, given the complexity of insurance products, we design a meta-path based method over insurance product knowledge graph. In source domain, we employ GRU to model users' dynamic interests. Then we learn a feature mapping function by multi-layer perceptions. We apply DCDIR on our company's dataset, and show DCDIR significantly outperforms the state-of-the-art solutions.