Keyword-level bayesian online bid optimization for sponsored search advertising

Abstract

Bid price optimization in Internet advertising is a very difficult task due to its high uncertainty. In this paper, we propose a bid price optimization algorithm focused on keyword-level bidding for pay-per-click sponsored search ads. The algorithm first predicts the performance of keywords as a distribution by modeling the relationship between ad metrics through a Bayesian network and performing Bayesian inference, and then outputs the bid price using a Bandit algorithm and online optimization. This approach enables online optimization that consideres uncertainty from the limited information available to advertisers. We conducted simulations on real data and confirmed the effectiveness of the proposed method on both open source data and data provided by an Internet ad-serving company.