冷启动(汽车)
领域(数学分析)
用户配置文件
人工智能
电子商务
协同过滤
数据挖掘
万维网
个性化
相似性(几何)
作者
Bi Ye,Liqiang Song,Mengqiu Yao,Wu Zhenyu,Wang Jianming,Xiao Jing
出处
期刊:Cornell University - arXiv
日期:2020-07-25
卷期号:: 1661-1664
被引量:13
标识
DOI:10.1145/3397271.3401193
摘要
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.
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