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

TL;DR

This paper introduces DCDIR, a deep cross-domain recommendation system tailored for cold start users in the insurance domain, addressing product complexity and leveraging knowledge graphs and dynamic interest modeling.

Contribution

The paper presents a novel deep learning framework combining knowledge graph-based feature extraction and interest modeling for cross-domain insurance recommendations.

Findings

DCDIR significantly outperforms existing solutions on real datasets.

Effective user and item feature learning improves cold start recommendations.

Meta path-based methods handle complex insurance products successfully.

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 could not 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 meta path based method over insurance product knowledge graph. In source domain, we employ GRU to model user dynamic interests. Then we learn a feature mapping function by multi-layer perceptions. We apply DCDIR on our company datasets, and show DCDIR significantly outperforms the state-of-the-art solutions.