CTR-Driven Advertising Image Generation with Multimodal Large Language Models

• URL: http://arxiv.org/abs/2502.06823v1
• Date: Wed, 05 Feb 2025 09:06:02 GMT
• Title: CTR-Driven Advertising Image Generation with Multimodal Large Language Models
• Authors: Xingye Chen, Wei Feng, Zhenbang Du, Weizhen Wang, Yanyin Chen, Haohan Wang, Linkai Liu, Yaoyu Li, Jinyuan Zhao, Yu Li, Zheng Zhang, Jingjing Lv, Junjie Shen, Zhangang Lin, Jingping Shao, Yuanjie Shao, Xinge You, Changxin Gao, Nong Sang,
• Abstract summary: We explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL) Our method achieves state-of-the-art performance in both online and offline metrics.
• Score: 53.40005544344148
• License:
• Abstract: In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily focus on the aesthetic quality, which may fail to achieve satisfactory online performance. To address this limitation, we explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. Firstly, we build targeted pre-training tasks, and leverage a large-scale e-commerce multimodal dataset to equip MLLMs with initial capabilities for advertising image generation tasks. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL), which can jointly utilize multimodal features and accurately reflect user click preferences. Meanwhile, a product-centric preference optimization strategy is developed to ensure that the generated background content aligns with the product characteristics after fine-tuning, enhancing the overall relevance and effectiveness of the advertising images. Extensive experiments have demonstrated that our method achieves state-of-the-art performance in both online and offline metrics. Our code and pre-trained models are publicly available at: https://github.com/Chenguoz/CAIG.

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