Enhanced Web User Interface Design Via Cross-Device Responsiveness Assessment Using An Improved HCI-INTEGRATED DL Schemes

TL;DR

This paper introduces a novel cross-device responsiveness assessment method for web UI optimization, employing advanced machine learning and optimization algorithms to enhance user experience and interface adaptability.

Contribution

It proposes an integrated framework combining FECSM, QNDSOA, and BiGLMRU for dynamic web UI optimization considering cross-responsiveness and user behavior patterns.

Findings

Achieved an average UI fitness of 98.56% with the proposed QNDSOA.

Effectively classified UX change types using BiGLMRU.

Enhanced user satisfaction through improved cross-device UI responsiveness.

Abstract

User Interface (UI) optimization is essential in the digital era to enhance user satisfaction in web environments. Nevertheless, the existing UI optimization models had overlooked the Cross-Responsiveness (CR) assessment, affecting the user interaction efficiency. Consequently, this article proposes a dynamic web UI optimization through CR assessment using Finite Exponential Continuous State Machine (FECSM) and Quokka Nonlinear Difference Swarm Optimization Algorithm (QNDSOA). Initially, the design and user interaction related information is collected as well as pre-processed for min-max normalization. Next, the Human-Computer Interaction (HCI)-based features are extracted, followed by user behaviour pattern grouping. Meanwhile, the CR assessment is done using FECSM. Then, the proposed Bidirectional Gated Luong and Mish Recurrent Unit (BiGLMRU) is used to classify the User eXperience (UX) change type, which is labelled based on the User Interface Change Prediction Index (UICPI). Lastly, a novel QNDSOA is utilized to optimize the UI design with an average fitness of 98.5632%. Feedback monitoring is done after optimal deployment.