ArXiv

TailorMind: Towards Preference-Aligned Multimodal Content Generation

2026-06-22 · 17:31 UTC ·Hengji Zhou, Ye Liu, Yufeng Liu... ·1 min read
Authors
Hengji Zhou, Ye Liu, Yufeng Liu...
Categories
cs.AI
arXiv
https://arxiv.org/abs/2606.23643v1
PDF
https://arxiv.org/pdf/2606.23643v1

Brief

TailorMind addresses personalized multimodal content generation when suitable user-generated content is absent by linking collaborative preference modeling with controllable multimodal generation. It enriches sparse histories via hypergraph collaborative filtering, refines textual profiles with ranking-error feedback and textual gradient descent, and uses retrieval-augmented style control plus cross-modal cohesion reflection. On TailorBench (three platforms, five evaluation axes) it improves novelty and aesthetic quality and yields up to 29% reranking Recall gains.

Why it matters

TailorMind produces personalized multimodal content that improves novelty and aesthetic quality over representative generation baselines and even ground-truth UGC, while achieving competitive or stronger coherence; experiments report up to 29% Recall gains in reranking.

Key details

  • TailorMind combines hypergraph collaborative filtering to enrich sparse user histories with textual-profile optimization using ranking-error feedback and textual gradient descent, plus retrieval-augmented style control and cross-modal cohesion reflection; evaluated on TailorBench (three mainstream platforms, five axes: coherence, novelty, aesthetic, hallucination, profiling).
Source evidence

Abstract

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.

Comment: 18 pages, 13 figures, 6 tables. Code available at https://github.com/iLearn-Lab/TailorMind