This audiobook is narrated by a digital voice.
This book explores how Artificial Intelligence (AI) and Reinforcement Learning (RL) are transforming search advertising by enabling real-time, data-driven ad targeting and bidding strategies.
It begins by examining the evolution of search advertising, the role of AI in digital marketing, and the challenges in optimizing ad campaigns. The fundamentals of Reinforcement Learning (RL) are explained, covering Markov Decision Processes (MDP), exploration-exploitation trade-offs, and key RL algorithms like Multi-Armed Bandits (MAB), Deep Q-Networks (DQN), and Policy Gradient Methods.
The economics of search advertising is explored, detailing auction models (GSP, VCG), bidding strategies, quality scores, and Return on Ad Spend (ROAS). The book then delves into how RL-powered systems are revolutionizing ad selection, bid optimization, and personalized ad delivery.
Data collection and feature engineering for AI-driven advertising is covered, including click-through rate (CTR) prediction, handling sparse data, and ethical considerations. Implementation aspects such as reward function design, training RL models, and real-time bidding (RTB) using AI are also discussed.
This audiobook is narrated by a digital voice.
This book explores how Artificial Intelligence (AI) and Reinforcement Learning (RL) are transforming search advertising by enabling real-time, data-driven ad targeting and bidding strategies.
It begins by examining the evolution of search advertising, the role of AI in digital marketing, and the challenges in optimizing ad campaigns. The fundamentals of Reinforcement Learning (RL) are explained, covering Markov Decision Processes (MDP), exploration-exploitation trade-offs, and key RL algorithms like Multi-Armed Bandits (MAB), Deep Q-Networks (DQN), and Policy Gradient Methods.
The economics of search advertising is explored, detailing auction models (GSP, VCG), bidding strategies, quality scores, and Return on Ad Spend (ROAS). The book then delves into how RL-powered systems are revolutionizing ad selection, bid optimization, and personalized ad delivery.
Data collection and feature engineering for AI-driven advertising is covered, including click-through rate (CTR) prediction, handling sparse data, and ethical considerations. Implementation aspects such as reward function design, training RL models, and real-time bidding (RTB) using AI are also discussed.
AI in Search Advertising: Reinforcement Learning for Optimized Ad Targeting
AI in Search Advertising: Reinforcement Learning for Optimized Ad Targeting
FREE
with a B&N Audiobooks Subscription
Product Details
| BN ID: | 2940194512812 |
|---|---|
| Publisher: | Anand Vemula |
| Publication date: | 02/09/2025 |
| Edition description: | Unabridged |
Videos
