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.

翻译：点击率（CTR）预测对于原生广告至关重要，但同时也具有挑战性，因为缺乏直接的查询意图。本文提出了一种大规模事件嵌入方案，通过在用户连续事件上使用弱监督训练孪生网络，对每个用户浏览事件进行编码。我们将CTR预测问题建模为有监督的循环神经网络，该网络自然地以事件序列形式建模用户历史。我们提出的循环模型利用预训练的事件嵌入向量和注意力层来建模用户历史。实验表明，我们的模型显著优于基线模型及其若干变体。