The Whole-Page Optimization via Dynamic Ad Allocation

Abstract

Modern search engines present result pages composed of two most prominent types of information: sponsored and organic search results. The whole-page results must satisfy user's information inquiry while sponsored ad alongside the search results has become a key monetization strategy for the platform. Against the backdrop of this situation, a basic question has received comparatively little attention: how many ads are good enough to get higher user satisfaction and better monetization Most search engines always display a fixed number of ads or use heuristic rules to determine the number of ads. In this paper, we formulate the task of finding the best number of ads into a linear programming optimization problem, for which we propose a novel online algorithm to solve. We have conducted several offline experiments and tested our approach in Alibaba E-commerce platform. The experimental results show that the platform could achieve higher revenue and more clicks simultaneously by the proposed algorithm.