iLearn-Lab

TailorMind: Preference-aligned multimodal content generation via hypergraph collaborative filtering

TailorMind is a new system for personalized multimodal content generation that translates user behavioral traces into generation-ready preferences without relying on existing item pools or user-generated content. The approach combines hypergraph collaborative filtering for sparse user histories, ranking-error feedback with textual gradient descent for profile optimization, and retrieval-augmented style control. The authors introduce TailorBench, a benchmark across three platforms evaluated on coherence, novelty, aesthetic quality, hallucination, and profiling, reporting up to 29% Recall gains in reranking over baselines.