  Historical features are important in ads click-through rate (CTR) prediction,
because they account for past engagements between users and ads. In this paper,
we study how to efficiently construct historical features through counting
features. The key challenge of such problem lies in how to automatically
identify counting keys. We propose a tree-based method for counting key
selection. The intuition is that a decision tree naturally provides various
combinations of features, which could be used as counting key candidate. In
order to select personalized counting features, we train one decision tree
model per user, and the counting keys are selected across different users with
a frequency-based importance measure. To validate the effectiveness of proposed
solution, we conduct large scale experiments on Twitter video advertising data.
In both online learning and offline training settings, the automatically
identified counting features outperform the manually curated counting features.
