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.
翻译:本文用概念知识图说明用户下一个意图预测的技术。 这个系统已经在Alipay的网上应用,每天为1亿以上的活跃用户服务。 具体地说,我们建议AlipayKG明确描述用户意图,这是生命-服务域的离线概念知识图,以模拟用户的历史行为、用户互动的丰富内容以及它们之间的关系。我们进一步引入一个基于变换器的模型,从知识图中整合专家规则,以推断在线用户的下一个意图。实验结果表明,拟议的系统可以有效地提高下游任务的绩效,同时保留解释性。