Dynamic optimal control models in advertising: recent developments
Management Science
On Continuous-Time Optimal Advertising Under S-Shaped Response
Management Science
On the Depth and Dynamics of Online Search Behavior
Management Science
A Trading Agent and Simulator for Keyword Auctions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Reinforcement Learning in Continuous Time and Space
Neural Computation
Optimal Dynamic Advertising Policy for New Products
Management Science
Budget optimization in search-based advertising auctions
Proceedings of the 8th ACM conference on Electronic commerce
Budget constrained bidding in keyword auctions and online knapsack problems
Proceedings of the 17th international conference on World Wide Web
A Knapsack Secretary Problem with Applications
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
Cyclical Bid Adjustments in Search-Engine Advertising
Management Science
Budget optimization for online campaigns with positive carryover effects
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Budget Strategy in Uncertain Environments of Search Auctions: A Preliminary Investigation
IEEE Transactions on Services Computing
| Hi-index | 0.00 |
As a form of targeted advertising, sponsored search auctions attract advertisers bidding for a limited number of slots in paid online listings. Sponsored search markets usually change rapidly over time, which requires advertisers to adjust their advertising strategies in a timely manner according to market dynamics. In this research, we argue that both the bid price and the advertiser (claimed) daily budget should be dynamically changed at a fine granularity (e.g., within a day) for an effective advertising strategy. By doing so, we can avoid wasting money on early ineffective clicks and seize better advertising opportunities in the future. We formulate the problem of dual adjusting (claimed) daily budget and bid price as a continuous state - discrete action decision process in the continuous reinforcement learning (CRL) framework. We fit the CRL approach to our decision scenarios by considering market dynamics and features of sponsored search auctions. We conduct experiments on a real-world dataset collected from campaigns conducted by an e-commerce advertiser on a major Chinese search engine to evaluate our dual adjustment strategy. Experimental results show that our strategy outperforms two state-of-the-art baseline strategies and illustrate the effect of adjusting either (claimed) daily budget or bid price in advertising.