  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.
