Comprehensive audience expansion based on end-To-end neural prediction

In current online advertising applications, look-Alike methods are valuable and commonly used to identify new potential users, tackling the difficulties of audience expansion. However, the demographic information and a variety of user behavior logs are high dimensional,noisy, and increasingly complex, which are challenging to extract suitable user profiles. Usually, rule-based and similaritybased approaches are proposed to profile the users' interests and expand the audience. However, they are specific and limited in more complex scenarios. In this paper, we propose a new end-To-end solution, unifying the feature extraction and profile prediction stages. Specifically, we present a neural prediction framework and leverage it with the intuitive audience feature extraction stages. We conduct extensive study on a real and large advertisement dataset. The results demonstrate the advantage of the proposed approach, not only in accuracy but also generality.