Large scale app recommendation in Ant Financial
Chaochao Chen, Xinxing Yang, Li Wang, Jun Zhou, Xiaolong Li · 2017
Nowadays, the number of mobile applications (Apps) is rapidly increasing due to different kinds of user needs. Existing App recommendation models can not scale to practical large data scenarios. In this paper, we present a large scale App recommendation framework, which has been applied into real application in Ant Financial for recommending Apps for hundreds of millions of users. We first present feature engineering, including a novel way to high-level features, and then use a linear ranking model to make recommendation. We also report the encouraging results through the experiments in real large scale dataset.