Ad Ranking

Ad ranking optimizes the ordering of advertisements to maximize various objectives, such as click-through rates, conversions, and advertiser revenue. Current research emphasizes improving ranking consistency across different stages of the ad serving process, leveraging multi-task and multi-scenario learning frameworks to handle diverse data sources and user contexts. These approaches often incorporate reinforcement learning, contrastive learning, and domain adaptation techniques to enhance model accuracy and efficiency, leading to significant improvements in ad placement and overall platform performance. The resulting advancements have direct implications for online advertising platforms and the broader field of recommender systems.