Intelligent Front-End Personalization: AI-Driven UI Adaptation
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915770371407872 |
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| author | Rajhans, Mona |
| author_facet | Rajhans, Mona |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_03154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intelligent Front-End Personalization: AI-Driven UI Adaptation Rajhans, Mona Human-Computer Interaction Artificial Intelligence Software Engineering 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. |
| title | Intelligent Front-End Personalization: AI-Driven UI Adaptation |
| topic | Human-Computer Interaction Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2602.03154 |