An industrial-scale system for heterogeneous information card ranking in alipay

Abstract

Alipay (https://global.alipay.com/), one of the world’s largest mobile and online payment platforms, provides not only payment services but also business about many aspects of our daily lives (finance, insurance, credit, express, news, social contact, etc.). The homepage in Alipay app (https://render.alipay.com/p/s/download) integrates massive heterogeneous information cards, which need to be ranked in appropriate order for better user experience. This paper demonstrates an industrial-scale system for heterogeneous information card ranking. We implement an ensemble ranking model, blending online and chunked-based learning algorithms which are developed on parameter server mechanism and able to handle industrial-scale data. Moreover, we propose efficient and effective factor embedding methods, which aim to reduce high-dimensional heterogenous factor features to low-dimensional embedding vectors by subtly revealing feature interactions. Offline experimental as well as online A/B testing results illustrate the efficiency and effectiveness of our proposals.