Keyword-Level Bayesian Online Bid Optimization for Sponsored Search Advertising

Abstract

Bid price optimization in online advertising is a challenging 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, which is a realistic setting for many firms. There are three characteristics of this setting: “The setting targets the optimization of bids for each keyword in pay-per-click sponsored search advertising”, “The only information available to advertisers is the number of impressions, clicks, conversions, and advertising cost for each keyword”, and “Advertisers bid daily and set monthly budgets on a campaign basis”. Our 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. Then, it outputs the bid price by means of a bandit algorithm and online optimization. This approach enables online optimization that considers uncertainty from the limited information available to advertisers. We conducted simulations using real data and confirmed the effectiveness of the proposed method for both open-source data and data provided by negocia, Inc., which provides an automated Internet advertising management system.