Optimizing e-commerce recommendation systems through conditional image generation: Merging LoRA and cGANs for improved performance

Abstract

This research concentrates on the integration of Low-Rank Adaptation for Text-to-Image Diffusion Fine-tuning and Conditional Image Generation in e-commerce recommendation systems. Low-Rank Adaptation for Text-to-Image Diffusion Fine-tuning, skilled in producing precise and diverse images from aesthetic descriptions provided by users, is extremely valuable for personalizing product suggestions. The enhancement of the interpretation of textual prompts and consequent image generation is accomplished through the fine-tuning of cross-attention layers in the Stable Diffusion model. In an effort to advance personalization further, Conditional Generative Adversarial Networks are employed to transform these textual descriptions into corresponding product images. In order to assure effective data communication, particularly in areas with low connectivity, the system makes use of Long Range technology, thereby improving system accessibility. Preliminary results demonstrate a considerable improvement in recommendation precision, user engagement, and conversion rates. These results underscore the potential impact of integrating such advanced artificial intelligence techniques in e-commerce, optimizing the shopping experience by generating personalized, accurate, and visually appealing product suggestions.