Hubble

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Abstract
Recently, in order to take a preemptive opportunity in the mobile economy, the Internet companies conduct thousands of marketing campaigns every day, to promote their mobile products and services. In the mobile marketing scenario, one of the fundamental issues is the audience expansion task for marketing campaigns. Given a set of seed users, audience expansion aims to seek more users (audiences), who are similar to the seeds and will finish the business goal of the targeted campaign (ie convert). However, the problem is challenging in three aspects. First, a company will run hundreds of campaigns to serve massive users every day. The requirements of scalability and timeliness make training model for each campaign extremely resource-consuming thus impractical. Therefore, we proposed to solve the problem in a two-stage manner, in which the offline stage employs heavyweight user representation learning and the online stage performs embedding-based lightweight audience expansion. Second, conventional two-stage audience expansion systems neglect the high-order user-campaign interactions and usually generate entangled user embeddings, thus fail to achieve high-quality user representation. Third, the seeds, which are usually provided by experts or collected from users' feedbacks, could be noisy and cannot cover the entire actual audiences, thus introduce coverage bias. Unfortunately, to our best knowledge, none of the related literatures tackle this crucial issue of audience expansion. Addressing the above challenges, in this paper, we present the Hubble System, an industrial solution for audience expansion in mobile marketing scenario. Hubble system follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, we propose a novel adaptive and disentangled graph neural network (called AD-GNN), to adaptively explore the user-campaign graph and generate comprehensive user embedding in a disentangled manner. In the online stage, tackling the coverage bias issue, we develop a novel audience expansion model with knowledge distillation mechanism (called KD-AE), to absorb knowledge from the offline AD-GNN and alleviate the coverage bias.Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed Hubble system, compared with other state-of-the-art methods.

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