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In Taobao, the largest e-commerce platform in China, billions of items areprovided and typically displayed with their images. For better user experienceand business effectiveness, Click Through Rate (CTR) prediction in onlineadvertising system exploits abundant user historical behaviors to identifywhether a user is interested in a candidate ad. Enhancing behaviorrepresentations with user behavior images will help understand user's visualpreference and improve the accuracy of CTR prediction greatly. So we propose tomodel user preference jointly with user behavior ID features and behaviorimages. However, training with user behavior images brings tens to hundreds ofimages in one sample, giving rise to a great challenge in both communicationand computation. To handle these challenges, we propose a novel and efficientdistributed machine learning paradigm called Advanced Model Server (AMS). Withthe well known Parameter Server (PS) framework, each server node handles aseparate part of parameters and updates them independently. AMS goes beyondthis and is designed to be capable of learning a unified image descriptor modelshared by all server nodes which embeds large images into low dimensional highlevel features before transmitting images to worker nodes. AMS thusdramatically reduces the communication load and enables the arduous jointtraining process. Based on AMS, the methods of effectively combining the imagesand ID features are carefully studied, and then we propose a Deep Image CTRModel. Our approach is shown to achieve significant improvements in both onlineand offline evaluations, and has been deployed in Taobao display advertisingsystem serving the main traffic.

Image Matters: Visually modeling user behaviors using Advanced Model Server