这篇论文发了TailorMind，能根据用户行为痕迹直接生成个性化多模态内容，不用等现成素材。在一致性、新颖性上超过现有方法，召回率提升29%。
这篇论文提出TailorMind，一种链接协同偏好建模与可控多模态生成的框架。它通过超图协同过滤丰富稀疏用户历史，并利用排序误差反馈和文本梯度下降优化文本档案。检索增强风格控制与跨模态一致性反射减少语义漂移。论文构建了TailorBench基准，从一致性、新颖性、美学、幻觉、画像五个维度评估。实验表明，TailorMind在多个维度超过现有生成基线和真实用户生成内容，重排序召回率提升29%。
TailorMind: Towards Preference-Aligned Multimodal Content Generation
Personalized content systems depend on available UGC and struggle when suitable content is absent, delayed, or costly to create. Although multimodal generators can synthesize content on demand, how to translate behavioral traces into generation-ready preferences remains underexplored. We study personalized multimodal content generation: creating user-tailored multimodal content without existing item pools or waiting for matching UGC. We propose TailorMind, linking collaborative preference modeling with controllable multimodal generation. TailorMind enriches sparse user histories via hypergraph collaborative filtering and optimizes textual profiles with ranking-error feedback and textual gradient descent. Retrieval-augmented style control grounds outputs in authentic UGC patterns, while cross-modal cohesion reflection reduces semantic drift. We construct TailorBench, a benchmark from three mainstream platforms evaluated along five dimensions: coherence, novelty, aesthetic, hallucination, profiling. Experiments show that TailorMind achieves competitive or stronger coherence, improves novelty and aesthetic quality over representative generation baselines and ground-truth UGC, demonstrating advantages over retrieving available content or comparable UGC, while achieving up to 29% Recall gains in reranking. Our code is released at: https://github.com/iLearn-Lab/TailorMind.