This research introduces Dynamic Target Isolation (DTI), a new training technique that significantly improves computational efficiency when using Large Language Models for click-through rate prediction.

• Addresses the high computational cost barrier of traditional LLM training for recommendation tasks
• Introduces a novel approach that isolates target context to reduce unnecessary computations
• Enables more efficient training on large-scale datasets
• Maintains prediction accuracy while dramatically reducing training time

This engineering breakthrough makes LLM-based recommendation systems more practical to implement at scale for real-world applications.

Towards An Efficient LLM Training Paradigm for CTR Prediction