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. Specifically, we propose AlipayKG to explicitly characterize user intent, 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.

该论文介绍了应用概念知识图谱进行用户下一步意图预测的技术。我们在支付宝的Web上部署了该系统，服务于超过1亿日活用户。为了明确用户的意图，我们提出了AlipayKG，这是一个离线的概念知识图谱，在生活服务领域建模用户的历史行为、用户交互的丰富内容以及它们之间的关系。我们进一步介绍了一种基于Transformer的模型，它将知识图谱中的专家规则与在线用户的下一个意图进行推断相结合。实验结果表明，该系统可以有效提高下游任务的性能，同时保持可解释性。

支付宝用户下一步意图预测的概念知识图谱