  This paper illustrates the technologies of user next intent prediction with a
concept knowledge graph. The system has been deployed on the Web at Alipay,
serving more than 100 million daily active users. To explicitly characterize
user intent, we propose AlipayKG, which is an offline concept knowledge graph
in the Life-Service domain modeling the historical behaviors of users, the rich
content interacted by users and the relations between them. We further
introduce a Transformer-based model which integrates expert rules from the
knowledge graph to infer the online user's next intent. Experimental results
demonstrate that the proposed system can effectively enhance the performance of
the downstream tasks while retaining explainability.
