Real-time Bidding Strategy in Display Advertising: An Empirical Analysis
This work addresses the challenge of efficient ad bidding for advertisers, but it is incremental as it builds on existing methods without introducing new paradigms.
The paper tackles the problem of optimizing real-time bidding strategies for display advertising by evaluating the performance of several representative strategies, including reinforcement learning-based approaches, on the iPinYou dataset, and provides suggestions for improvement.
Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representative bidding strategies are introduced, especially the research advances and challenges of reinforcement learning-based bidding strategies. Further, we quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset. Specifically, we examine the effects of state, action, and reward function on the performance of reinforcement learning-based bidding strategies. Finally, we summarize the general steps for optimizing bidding strategies using reinforcement learning algorithms and present our suggestions.