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arXiv · 1804.09133

Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks

Mehul Parsana·Krishna Poola·Yajun Wang·Zhiguang Wang
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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.

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Mehul Parsana, Krishna Poola, Yajun Wang, Zhiguang Wang. 2018-04-24. Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks. https://arxiv.org/abs/1804.09133

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