The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timelines instead of individual bid requests, manipulating full policies (i.e. pre-defined bidding strategies) and not bid values. In order to optimally allocate policies to users, typical multiple treatments allocation methods solve knapsack-like problems which aim at maximizing an expected value under constraints. In the industrial contexts such as online advertising, we argue that optimizing for the probability of success is a more suited objective than expected value maximization, and we introduce the SuccessProbaMax algorithm that aims at finding the policy allocation which is the most likely to outperform a fixed reference policy. Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate.

翻译：电子商务广告的有效性在很大程度上取决于商家为其目标用户出价并赢得曝光机会的能力。由于市场竞争、用户行为以及广告主多样化目标等多种因素，出价过程极为复杂。本文从用户时间线（而非单个出价请求）层面考虑问题，操控完整策略（即预定义的出价策略）而非具体出价值。为了将策略最优地分配给用户，典型的多重处理分配方法需解决此类背包问题，其目标是在约束条件下最大化期望值。在在线广告等工业场景中，我们认为优化成功概率比最大化期望值更为合适，并提出了SuccessProbaMax算法，该算法旨在找到最有可能超越固定参考策略的策略分配方案。最后，我们在合成数据和真实数据上进行了全面实验以评估其性能。结果表明，所提算法在成功率上优于传统的期望值最大化算法。