Navigating User Behavior toward Personalized Multimodal Generation

The paper introduces NaviGen, a novel approach for personalized multimodal content generation that enhances alignment between user intent and generated outputs. It utilizes a dual identifier system combining collaborative and textual codes to encode user behavior, followed by a two-stage training pipeline of supervised fine-tuning (SFT) and reinforcement learning (RL) to improve instruction writing and preference reasoning. Experimental results demonstrate that NaviGen significantly enhances the quality of personalized image and video generation, making it a valuable tool for practitioners seeking to refine user interaction in AI-generated content.