Yilei Wang
ABSTRACT
Sponsored search has been recognized as one of the major internet monetization solutions for commercial search engines. There are generally three types of participants in this online advertising problem, who are search users, advertisers and publishers. Though previous studies have proposed to optimize for different participants independently, it is underexplored how to optimize for all participants in a unified framework and in a systematic way. In this paper, we propose to model the ad ranking problem in sponsored search as a Multi-Objective Optimization (MOO) problem for all participants. We show that many previous studies are special cases of the MOO framework. Taking advantage from the Pareto solution set of MOO, we can easily find more optimized solutions with significant improvement in one objective and minor sacrifice in others. This enables a more flexible way for us to tradeoff among different participants, i.e. objective functions, in sponsored search. Besides the empirical studies for comparing MOO with related previous sponsored search studies, we provide the insightful applications of MOO framework, which is a prediction model to help users determine the tradeoff parameters among different objective functions. Experimental results show the outstanding performance of the proposed prediction model for parameter selection in ad ranking optimization.
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Index Terms
- Multi-objective optimization for sponsored search
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