تقرير
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners
العنوان: | Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners |
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المؤلفون: | Vashishtha, Shanu, Prakash, Abhinav, Morishetti, Lalitesh, Nag, Kaushiki, Arora, Yokila, Kumar, Sushant, Achan, Kannan |
سنة النشر: | 2024 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Computer Science - Machine Learning |
الوصف: | Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ a manual process to generate the banners, which is time-consuming and has limitations of scalability. In this work, we demonstrate the use of text-to-image models for generating personalized web banners with dynamic content for online shoppers based on their interactions. The novelty in this approach lies in converting users' interaction data to meaningful prompts without human intervention. To this end, we utilize a large language model (LLM) to systematically extract a tuple of attributes from item meta-information. The attributes are then passed to a text-to-image model via prompt engineering to generate images for the banner. Our results show that the proposed approach can create high-quality personalized banners for users. Comment: 10 pages |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2403.05578 |
رقم الأكسشن: | edsarx.2403.05578 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |