The growth of e-commerce has led to the widespread use DeepCTR technology. Among various types, deep interest network (DIN), evolution (DIEN), and session (DSIN) developed by Alibaba have achieved good results in practice. However, above model’s filtering for user’s own historical behavior sequences insufficient context features lead reduced recommendation effectiveness. To address these issues...

Click-through rate is a quantity of interest to online advertisers, search engine optimizers, and sponsored search providers alike. The rate at which users click on advertisements presented to them serves as both a metric for evaluating advertising effectiveness and a financial tool for cost and revenue projection. To predict future click-through rate, investigators typically use historical cli...

Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown promising way to enhance predictive performance. Even though various models are able learn feature interactions without manual engineering, they rarely attempt individually representations for different structures. In particula...

Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems. Existing methods usually model user behaviors, while ignoring the informative context which influences to make a click decision, e.g., pages pre-ranking candidates that inform inferences about interests, leading suboptimal performance. In this paper, we propose Decision-Making Context Interaction Ne...