  In Taobao, the largest e-commerce platform in China, billions of items are
provided and typically displayed with their images. For better user experience
and business effectiveness, Click Through Rate (CTR) prediction in online
advertising system exploits abundant user historical behaviors to identify
whether a user is interested in a candidate ad. Enhancing behavior
representations with user behavior images will help understand user's visual
preference and improve the accuracy of CTR prediction greatly. So we propose to
model user preference jointly with user behavior ID features and behavior
images. However, training with user behavior images brings tens to hundreds of
images in one sample, giving rise to a great challenge in both communication
and computation. To handle these challenges, we propose a novel and efficient
distributed machine learning paradigm called Advanced Model Server (AMS). With
the well known Parameter Server (PS) framework, each server node handles a
separate part of parameters and updates them independently. AMS goes beyond
this and is designed to be capable of learning a unified image descriptor model
shared by all server nodes which embeds large images into low dimensional high
level features before transmitting images to worker nodes. AMS thus
dramatically reduces the communication load and enables the arduous joint
training process. Based on AMS, the methods of effectively combining the images
and ID features are carefully studied, and then we propose a Deep Image CTR
Model. Our approach is shown to achieve significant improvements in both online
and offline evaluations, and has been deployed in Taobao display advertising
system serving the main traffic.
