Title: Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks,
Abstract: Click through rate (CTR) prediction is very important for Native
advertisement but also hard as there is no direct query intent. In this paper
we propose a large-scale event embedding scheme to encode the each user
browsing event by training a Siamese network with weak supervision on the
users' consecutive events. The CTR prediction problem is modeled as a
supervised recurrent neural network, which naturally model the user history as
a sequence of events. Our proposed recurrent models utilizing pretrained event
embedding vectors and an attention layer to model the user history. Our
experiments demonstrate that our model significantly outperforms the baseline
and some variants.