As one of the largest B2C e-commerce platforms in China, JD com also powers a
leading advertising system, serving millions of advertisers with fingertip
connection to hundreds of millions of customers. In our system, as well as most
e-commerce scenarios, ads are displayed with images.This makes visual-aware
Click Through Rate (CTR) prediction of crucial importance to both business
effectiveness and user experience. Existing algorithms usually extract visual
features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse
the visual and non-visual features for the finally predicted CTR. Despite being
extensively studied, this field still face two key challenges. First, although
encouraging progress has been made in offline studies, applying CNNs in real
systems remains non-trivial, due to the strict requirements for efficient
end-to-end training and low-latency online serving. Second, the off-the-shelf
CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf
CNNs were designed for classification thus never take categories as input
features. While in e-commerce, categories are precisely labeled and contain
abundant visual priors that will help the visual modeling. Unaware of the ad
category, these CNNs may extract some unnecessary category-unrelated features,
wasting CNN's limited expression ability. To overcome the two challenges, we
propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early
incorporates the category knowledge with a light-weighted attention-module on
each convolutional layer. This enables CSCNN to extract expressive
category-specific visual patterns that benefit the CTR prediction. Offline
experiments on benchmark and a 10 billion scale real production dataset from
JD, together with an Online A/B test show that CSCNN outperforms all compared
state-of-the-art algorithms