Two-Stage Audience Expansion for Financial Targeting in Marketing

With the revolution of mobile internet, online finance has grown explosively. In this new area, one challenge of significant importance is how to effectively deliver the financial products or services to a set of target users by marketing. Given a product or service to be promoted and a set of users as seeds, audience expansion is such a targeting technique, which aims to find potential audience among a large number of users. However, in the context of finance, financial products and services are dynamic in nature as they co-vary with the socio-economic environment. Moreover, marketing campaigns for promoting products or services always consist of different rules of play, even for the same type of products or services. As a result, there is a strong demand for the timeliness of seeds in financial targeting. Conventional one-stage audience expansion methods, which generate expanded users by expanding over seeds, would encounter two problems under this setting: (1) the seeds would inevitably involve a number of users that are not representative for expansion, and direct expansion over these noisy seeds would dramatically deteriorate the performance; (2) one-stage expansion over fixed seeds cannot timely and accurately capture users' preferences over the currently running campaign due to the lack of timeliness of seeds.To address the above challenges, in this paper, we present a novel two-stage audience expansion system Hubble. In the first cold-start stage, a reweighting mechanism is devised to suppress the noises within seeds, which is motivated from the observation on the relationship between golden seeds and their corresponding density in the embedding space. With incrementally collecting feedbacks from users, we further include these feedbacks to guide subsequent audience expansion in the second stage. But the distribution of these feedbacks is usually biased and cannot fully characterize the distribution of all target audiences. Therefore, we propose a method to incorporate biased feedbacks with seeds in a meta-learning manner to pan for golden seeds from the noisy seed-set. Finally, we conduct extensive experiments on three real datasets and online A/B testing, which demonstrate the effectiveness of the proposed method. In addition, we release two datasets for boosting the study of this new research topic.