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arXiv · 2604.20434

Discrete Preference Learning for Personalized Multimodal Generation

Yuting Zhang·Ying Sun·Dazhong Shen·Ziwei Xie·Feng Liu·Changwang Zhang·Xiang Liu·Jun Wang·Hui Xiong
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Abstract

The emergence of generative models enables the creation of texts and images tailored to users' preferences. Existing personalized generative models have two critical limitations: lacking a dedicated paradigm for accurate preference modeling, and generating unimodal content despite real-world multimodal-driven user interactions. Therefore, we propose personalized multimodal generation, which captures modal-specific preferences via a dedicated preference model from multimodal interactions, and then feeds them into downstream generators for personalized multimodal content. However, this task presents two challenges: (1) Gap between continuous preferences from dedicated modeling and discrete token inputs intrinsic to generator architectures; (2) Potential inconsistency between generated images and texts. To tackle these, we present a two-stage framework called Discrete Preference learning for Personalized Multimodal Generation (DPPMG). In the first stage, to accurately learn discrete modal-specific preferences, we introduce a modal-specific graph neural network (a dedicated preference model) to learn users' modal-specific preferences, which preferences are then quantized into discrete preference tokens. In the second stage, the discrete modal-specific preference tokens are injected into downstream text and image generators. To further enhance cross-modal consistency while preserving personalization, we design a cross-modal consistent and personalized reward to fine-tune token-associated parameters. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model in generating personalized and consistent multimodal content.

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Yuting Zhang, Ying Sun, Dazhong Shen, Ziwei Xie, Feng Liu, Changwang Zhang, Xiang Liu, Jun Wang, Hui Xiong. 2026-04-22. Discrete Preference Learning for Personalized Multimodal Generation. https://doi.org/10.1145/3805712.3809552

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