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arXiv · 2405.13381

Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding

Chang Zhou·Yang Zhao·Jin Cao·Yi Shen·Xiaoling Cui·Chiyu Cheng
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Abstract

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios.

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Chang Zhou, Yang Zhao, Jin Cao, Yi Shen, Xiaoling Cui, Chiyu Cheng. 2024-05-22. Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding. https://arxiv.org/abs/2405.13381

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