[KDD 2020] Category-Specific CNN for Visual-aware CTR Prediction at JD.com - CrossMinds.ai
[KDD 2020] Category-Specific CNN for Visual-aware CTR Prediction at JD.com
Aug 13, 2020141 views
Hu Liu
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. We also build a highly efficient infrastructure to accomplish end-to-end training with CNN

Paper Url: https://dl.acm.org/doi/10.1145/3394486.3403319

Authors: Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan @JD.com
Applied Research