Maximizing the Success Probability of Policy Allocations in Online Systems
This addresses the challenge for merchants in e-commerce to improve ad effectiveness by focusing on success probability, though it is incremental as it adapts existing allocation methods to a new objective.
The paper tackles the problem of allocating bidding strategies to users in online advertising by optimizing for the probability of success rather than expected value, and shows that their SuccessProbaMax algorithm outperforms conventional methods in success rate.
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.