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Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Xiaotian Hao · Zhaoqing Peng · Yi Ma · Guan Wang · Junqi Jin · Jianye Hao · Shan Chen · Rongquan Bai · Mingzhou Xie · Miao Xu · Zhenzhe Zheng · Chuan Yu · HAN LI · Jian Xu · Kun Gai

Tue Jul 14 07:00 AM -- 07:45 AM & Tue Jul 14 06:00 PM -- 06:45 PM (PDT) @

In E-commerce, advertising is essential for merchants to reach their target users. The typical objective is to maximize the advertiser's cumulative revenue over a period of time under a budget constraint. In real applications, an advertisement (ad) usually needs to be exposed to the same user multiple times until the user finally contributes revenue (e.g., places an order). However, existing advertising systems mainly focus on the immediate revenue with single ad exposures, ignoring the contribution of each exposure to the final conversion, thus usually falls into suboptimal solutions. In this paper, we formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space while ensuring the solution quality. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach. Extensive offline and online experiments show the superior performance of our approaches over state-of-the-art baselines in terms of cumulative revenue.

Author Information

Xiaotian Hao (College of Intelligence and Computing, Tianjin University)
Zhaoqing Peng (Alibaba Group)
Yi Ma (Tianjin University)
Guan Wang (Department of Automation, Tsinghua University)
Junqi Jin (Alibaba Group)
Jianye Hao (Tianjin University)
Shan Chen (Alibaba Group)
Rongquan Bai (Alibaba Group)
Mingzhou Xie (Alibaba Group)
Miao Xu (Alibaba Group)
Zhenzhe Zheng (Shanghai Jiao Tong University)
Chuan Yu (Alibaba Group)
HAN LI (Alibaba Group)
Jian Xu (Alibaba Group)
Kun Gai (Alibaba group)

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