Power of Suggestion: Strategic Feature Manipulation in Transformer-Based Models

This paper presents ASGARD, a novel generative framework for personalized advertising strategy design. ASGARD integrates user-preferences while maintaining the ability to autonomously generate high-quality outputs. The core challenge is balancing user preferences with the model's knowledge, enabling it to align with user inputs or propose optimal alternatives. Our novel approach uses a token-driven method for strategy generation, adapts a token masking strategy for training, and refines the loss function to prevent issues like mode collapse. We evaluate ASGARD to demonstrate its effectiveness, identify limitations, and suggest future enhancements. To our knowledge, this is the first approach that meets all constraints of advertising strategy design while allowing user-preference integration, showing potential for generalization across transformer-based generative models.