  A contemporary feed application usually provides blended results of organic
items and sponsored items~(ads) to users. Conventionally, ads are exposed at
fixed positions. Such a static exposure strategy is inefficient due to ignoring
users' personalized preferences towards ads. To this end, adaptive ad exposure
has become an appealing strategy to boost the overall performance of the feed.
However, existing approaches to implementing the adaptive ad exposure still
suffer from several limitations: 1) they usually fall into sub-optimal
solutions because of only focusing on request-level optimization without
consideration of the long-term application-level performance and constraints,
2) they neglect the necessity of keeping the game-theoretical properties of ad
auctions, which may lead to anarchy in bidding, and 3) they can hardly be
deployed in large-scale applications due to high computational complexity. In
this paper, we focus on long-term performance optimization under hierarchical
constraints in feeds and formulate the adaptive ad exposure as a Dynamic
Knapsack Problem. We propose an effective approach: Hierarchically Constrained
Adaptive Ad Exposure~(HCA2E). We present that HCA2E possesses desired
game-theoretical properties, computational efficiency, and performance
robustness. Comprehensive offline and online experiments on a leading
e-commerce application demonstrate the significant performance superiority of
HCA2E over representative baselines. HCA2E has also been deployed on this
application to serve millions of daily users.
