ROI constrained optimal online allocation in sponsored search.

Sponsored search plays a major role in the revenue contribution of e-commerce platforms. Advertising systems are designed to maximize platform revenue, but other goals also need to be considered, such as user experience, advertiser utility, and how to achieve the long-term revenue goal. A key component of a sponsored search system is online allocation, which makes real-time decisions to match users' search requests with relevant ad campaigns to maximize platform revenue within constraints such as campaign budgets. Although much progress has been made, most of the research work on allocation problem has focused on satisfying guaranteed deals for display ads, and those challenges for allocation problems in sponsored search are not properly addressed. In this paper, we develop a framework to solve the large-scale sponsored search ad allocation problem, consisting of two main parts. One is an optimization problem solved offline by a parameter-server based architecture, and the other is an online strategy to alleviate the conflict with the auction mechanism during online service. Comprehensive offline evaluation on real production data and online A/B testing on real production system have been made. The experimental results demonstrate that through better allocating user queries to appropriate ads, the proposed model can significantly increase the platform's revenue without sacrificing advertisers' ROI.