# Intelligent Front-End Personalization: AI-Driven UI Adaptation

arXiv: https://arxiv.org/abs/2602.03154 · HTML: https://mlsanity.com/paper/2602.03154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 1 of 1)

- [Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization](https://mlsanity.com/paper/2412.16837/) (2024)

## Abstract (arXiv)

Front-end personalization has traditionally relied on static designs or rule-based adaptations, which fail to fully capture user behavior patterns. This paper presents an AI driven approach for dynamic front-end personalization, where UI layouts, content, and features adapt in real-time based on predicted user behavior. We propose three strategies: dynamic layout adaptation using user path prediction, content prioritization through reinforcement learning, and a comparative analysis of AI-driven vs. rule-based personalization. Technical implementation details, algorithms, system architecture, and evaluation methods are provided to illustrate feasibility and performance gains.