A Concept Knowledge Graph for User Next Intent Prediction at Alipay
Yacheng He, Qianghuai Jia, Lin Yuan, Ruopeng Li, Yixin Ou, Ningyu, Zhang

TL;DR
This paper presents AlipayKG, a concept knowledge graph and a Transformer-based model for predicting user next intent, deployed at Alipay to serve over 100 million users, improving task performance and explainability.
Contribution
Introduction of AlipayKG, an offline concept knowledge graph, and a Transformer-based model integrating expert rules for accurate, explainable user intent prediction.
Findings
Effective enhancement of downstream task performance
Retains explainability in intent prediction
Deployed at scale for over 100 million users
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Recommender Systems and Techniques · Topic Modeling
