Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that, e-commerce platforms usually have multiple entrances for different categories and some entrances have few visits. Data from these entrances has low coverage, which makes it difficult for the agent to learn. To address this challenge, we propose Similarity-based Hybrid Transfer for Ads Allocation (SHTAA), which effectively transfers samples as well as knowledge from data-rich entrance to data-poor entrance. Specifically, we define an uncertainty-aware similarity for MDP to estimate the similarity of MDP for different entrances. Based on this similarity, we design a hybrid transfer method, including instance transfer and strategy transfer, to efficiently transfer samples and knowledge from one entrance to another. Both offline and online experiments on Meituan food delivery platform demonstrate that the proposed method could achieve better performance for data-poor entrance and increase the revenue for the platform.
翻译:为了最大限度地增加平台收入,向有限的供养名额分配广告和有机物品,这种分配已成为一个研究热点。请注意,电子商务平台通常有不同类别的多个入口,有些入口很少访问。这些入口的数据覆盖面低,使代理商难以了解。为了应对这一挑战,我们提议采用基于相似性的混合分配分配办法(SHTAA),有效地将样品和知识从数据贫乏的入口输入数据丰富的入口转移过来。具体地说,我们定义了多边发展方案的不确定性相似性,以估计不同入口的MDP相似性。基于这一相似性,我们设计了一种混合转移方法,包括案例转移和战略转移,以便有效地将样品和知识从一个入口转移到另一个入口。在Meituan食品交付平台上进行的离线和在线实验都表明,拟议的方法可以提高数据贫乏入口的绩效并增加平台的收入。