Hierarchically Constrained Adaptive Ad Exposure in Feeds

Abstract

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 fixed ad exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end,adaptive ad exposure is becoming an appealing strategy to boost the overall performance of the feed. However, existing approaches to implement the adaptive ad exposure strategy suffer from several limitations: 1) they usually fall into sub-optimal solutions because of only focusing on request-level optimization without consideration of the application-level performance and constraints, 2) they neglect the necessity of keeping the game-theoretical properties of ad auctions, and 3) they can hardly be deployed in large-scale applications due to high computational complexity. In this paper, we focus on the application-level performance optimization under hierarchical constraints in feeds and formulate adaptive ad exposure as a Dynamic Knapsack Problem. We propose Hierarchically Constrained Adaptive Ad Exposure∼(HCA2E) that possesses the desirable game-theoretical properties, computational efficiency, and performance robustness. Comprehensive offline and online experiments on a leading e-commerce application demonstrate the performance superiority of HCA2E.