论文标题
DCDIR：在保险领域中的冷启动用户的深层域推荐系统
DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain
论文作者
论文摘要
互联网保险产品显然与传统的电子商务商品的复杂性，低购买频率等不同。因此，冷启动问题甚至更糟。在传统的电子商务领域中，已经研究了几种跨域建议（CDR）方法，以根据其他域中的偏好来推断冷启动用户的偏好。但是，由于产品的复杂性，这些CDR方法无法直接应用于保险领域。在本文中，我们为冷启动用户提出了一个深层域保险推荐系统（DCDIR）。具体来说，我们首先在两个域中学习更有效的用户和项目潜在功能。在目标域中，鉴于保险产品的复杂性，我们设计了基于元路径的方法，而不是保险产品知识图。在源域中，我们采用GRU来建模用户动态兴趣。然后，我们通过多层知觉学习功能映射函数。我们在公司数据集上应用DCDIR，并显示DCDIR的表现明显优于最先进的解决方案。
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.
