AI-Driven Personalization and Recommendations for Web Design Resources

AI-Driven Personalization and Recommendations for Web Design Resources

Mohit Supolia, Durgesh Kumar Nishad, Mubashir Hassan, Vineet Kumar Chauhan

Department of Information Technology, Galgotias College of Engineering And Technology, Greater Noida, Uttar Pradesh, India

Abstract

The integration of Artificial Intelligence (AI) into creative workflows is transforming web design by enabling more personalized and efficient experiences. This study explores the development of AI-driven recommendation systems that suggest icons, fonts, and images based on designers' preferences, project needs, and current design trends. The proposed framework combines collaborative filtering, content-based analysis, and contextual modeling to adapt to changing user requirements while encouraging both familiarity and creative exploration.

An experimental study with 127 professional web designers showed a 37% reduction in element selection time, a 28% improvement in design coherence, and 82% reported enhanced creativity. Evaluation metrics included adoption rates, workflow efficiency, and creative diversity. Results highlight how AI recommendation systems can act as creative collaborators—enhancing, not replacing, human input. This work advances the field of AI- assisted design by presenting a balanced, adaptive framework that supports creative autonomy in modern web design.

Keywords: Artificial intelligence, recommendation systems, web design, personalization, creative tools, user experience, workflow optimization.