Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces

Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces

Authors

  • Yingchia Liu Parsons School of Design, MFA Design and Technology, NY, USA
  • Hao Tan Computer Science and Technology, China University of Geosciences, Bejing, China
  • Guanghe Cao Computer Science, University of Southern California, CA,USA
  • Yang Xu Interactive Telecommunications Program, New York University, NY,USA

DOI:

https://doi.org/10.53469/wjimt.2024.07(05).01

Keywords:

Adaptive UI/UX, User Engagement, Context-Aware Adaptation, Mobile Application Design

Abstract

This paper presents a comprehensive study on developing and evaluating an adaptive UI/UX framework to enhance user engagement in mobile applications through personalized interfaces. The research investigates key factors influencing user engagement, including demographics, cognitive abilities, and contextual variables. A context-aware adaptation engine was designed to adjust interface elements based on real-time user data dynamically. The proposed framework was implemented in a mobile learning application and subjected to rigorous usability testing and user engagement analysis. Results demonstrated significant improvements in task completion rates, user satisfaction, and overall engagement metrics compared to non-adaptive interfaces. This study contributes valuable insights into the design and optimization of adaptive mobile interfaces, emphasizing the importance of personalization in creating compelling user experiences.

References

Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.

Shi J, Shang F, Zhou S, et al. Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy[J]. Journal of Industrial Engineering and Applied Science, 2024, 2(4): 90-103.

Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 108-126.

Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.

Lei, H., Wang, B., Shui, Z., Yang, P., & Liang, P. (2024). Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology. arXiv preprint arXiv:2404.04492.

Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.

Xu, Y., Liu, Y., Xu, H., & Tan, H. (2024). AI-Driven UX/UI Design: Empirical Research and Applications in FinTech. International Journal of Innovative Research in Computer Science & Technology, 12(4), 99-109.

Liu, Y., Xu, Y., & Song, R. (2024). Transforming User Experience (UX) through Artificial Intelligence (AI) in interactive media design. Engineering Science & Technology Journal, 5(7), 2273-2283.

Zhang, P. (2024). A STUDY ON THE LOCATION SELECTION OF LOGISTICS DISTRIBUTION CENTERS BASED ON E-COMMERCE. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 103-107.

Zhang, P., & Gan, L. I. U. (2024). Optimization of Vehicle Scheduling for Joint Distribution in Logistics Park based on Priority. Journal of Industrial Engineering and Applied Science, 2(4), 116-121.

Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-world Data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69.

Ping, G., Wang, S. X., Zhao, F., Wang, Z., & Zhang, X. (2024). Blockchain Based Reverse Logistics Data Tracking: An Innovative Approach to Enhance E-Waste Recycling Efficiency.

Xu, H., Niu, K., Lu, T., & Li, S. (2024). Leveraging artificial intelligence for enhanced risk management in financial services: Current applications and future prospects. Engineering Science & Technology Journal, 5(8), 2402-2426.

Shi, Y., Shang, F., Xu, Z., & Zhou, S. (2024). Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores. Journal of Artificial Intelligence and Development, 3(1), 40-46.

Wang, Shikai, Kangming Xu, and Zhipeng Ling. "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 77-87.

Ping, G., Zhu, M., Ling, Z., & Niu, K. (2024). Research on Optimizing Logistics Transportation Routes Using AI Large Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 14-27.

Shang, F., Shi, J., Shi, Y., & Zhou, S. (2024). Enhancing E-Commerce Recommendation Systems with Deep Learning-based Sentiment Analysis of User Reviews. International Journal of Engineering and Management Research, 14(4), 19-34.

Xu, H., Li, S., Niu, K., & Ping, G. (2024). Utilizing Deep Learning to Detect Fraud in Financial Transactions and Tax Reporting. Journal of Economic Theory and Business Management, 1(4), 61-71.

Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. arXiv preprint arXiv:2403.19345.

Zheng, H., Xu, K., Zhou, H., Wang, Y., & Su, G. (2024). Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis. Academic Journal of Science and Technology, 10(1), 62-68.

Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Applied and Computational Engineering 2024, 87, 26–32,

Zhan, X., Shi, C., Li, L., Xu, K., & Zheng, H. (2024). Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models. Applied and Computational Engineering, 71, 21-26.

Liu, B., Zhao, X., Hu, H., Lin, Q., & Huang, J. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science, 3(12), 36-42.

Li, P., Hua, Y., Cao, Q., & Zhang, M. (2020, December). Improving the Restore Performance via Physical-Locality Middleware for Backup Systems. In Proceedings of the 21st International Middleware Conference (pp. 341-355).

Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). THE IMPACT OF PRICING SCHEMES ON CLOUD COMPUTING AND DISTRIBUTED SYSTEMS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 193-205.

Shang, F., Zhao, F., Zhang, M., Sun, J., & Shi, J. (2024). Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences. International Journal of Innovative Research in Engineering and Management, 11(4), 39-49.

Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Applied and Computational Engineering, 67, 1-7.

Wu, B., Gong, Y., Zheng, H., Zhang, Y., Huang, J., & Xu, J. (2024). Enterprise cloud resource optimization and management based on cloud operations. Applied and Computational Engineering, 67, 8-14.

Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J., & Wan, W. (2024). Application of machine learning optimization in cloud computing resource scheduling and management. Applied and Computational Engineering, 64, 9-14.

Liu, B., & Zhang, Y. (2023). Implementation of seamless assistance with Google Assistant leveraging cloud computing. Journal of Cloud Computing, 12(4), 1-15.

Guo, L., Li, Z., Qian, K., Ding, W., & Chen, Z. (2024). Bank Credit Risk Early Warning Model Based on Machine Learning Decision Trees. Journal of Economic Theory and Business Management, 1(3), 24-30.

Xu, Z., Guo, L., Zhou, S., Song, R., & Niu, K. (2024). Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 1-7.

Song, R., Wang, Z., Guo, L., Zhao, F., & Xu, Z. (2024). Deep Belief Networks (DBN) for Financial Time Series Analysis and Market Trends Prediction.World Journal of Innovative Medical Technologies, 5(3), 27-34.

Guo, L.; Song, R.; Wu, J.; Xu, Z.; Zhao, F. Integrating a Machine Learning-Driven Fraud Detection System Based on a Risk Management Framework. Preprints 2024, 2024061756.

Feng, Y., Qi, Y., Li, H., Wang, X., & Tian, J. (2024, July 11). Leveraging federated learning and edge computing for recommendation systems within cloud computing networks. In Proceedings of the Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024) (Vol. 13210, pp. 279-287). SPIE.

Gong, Y., Liu, H., Li, L., Tian, J., & Li, H. (2024, February 28). Deep learning-based medical image registration algorithm: Enhancing accuracy with dense connections and channel attention mechanisms. Journal of Theory and Practice of Engineering Science, 4(02), 1-7.

Yu, K., Bao, Q., Xu, H., Cao, G., & Xia, S. (2024). An Extreme Learning Machine Stock Price Prediction Algorithm Based on the Optimisation of the Crown Porcupine Optimisation Algorithm with an Adaptive Bandwidth Kernel Function Density Estimation Algorithm.

Li A, Zhuang S, Yang T, Lu W, Xu J. Optimization of logistics cargo tracking and transportation efficiency based on data science deep learning models. Applied and Computational Engineering. 2024 Jul 8;69:71-7.

Xu, J., Yang, T., Zhuang, S., Li, H. and Lu, W., 2024. AI-based financial transaction monitoring and fraud prevention with behaviour prediction. Applied and Computational Engineering, 77, pp.218-224.

Ling, Z., Xin, Q., Lin, Y., Su, G. and Shui, Z., 2024. Optimization of autonomous driving image detection based on RFAConv and triplet attention. Applied and Computational Engineering, 77, pp.210-217.

He, Z., Shen, X., Zhou, Y., & Wang, Y. (2024, January). Application of K-means clustering based on artificial intelligence in gene statistics of biological information engineering. In Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing (pp. 468-473).

Gong, Y., Zhu, M., Huo, S., Xiang, Y., & Yu, H. (2024, March). Utilizing Deep Learning for Enhancing Network Resilience in Finance. In 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) (pp. 987-991). IEEE.

Xin, Q., Xu, Z., Guo, L., Zhao, F., & Wu, B. (2024). IoT traffic classification and anomaly detection method based on deep autoencoders. Applied and Computational Engineering, 69, 64-70.

Yang, T., Li, A., Xu, J., Su, G. and Wang, J., 2024. Deep learning model-driven financial risk prediction and analysis. Applied and Computational Engineering, 77, pp.196-202.

Zhou, Y., Zhan, T., Wu, Y., Song, B., & Shi, C. (2024). RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models. arXiv preprint arXiv:2405.06655.

Liu, B., Cai, G., Ling, Z., Qian, J., & Zhang, Q. (2024). Precise Positioning and Prediction System for Autonomous Driving Based on Generative Artificial Intelligence. Applied and Computational Engineering, 64, 42-49.

Cui, Z., Lin, L., Zong, Y., Chen, Y., & Wang, S. (2024). Precision Gene Editing Using Deep Learning: A Case Study of the CRISPR-Cas9 Editor. Applied and Computational Engineering, 64, 134-141.

Zhang, X., 2024. Machine learning insights into digital payment behaviors and fraud prediction. Applied and Computational Engineering, 67, pp.61-67.

Zhang, X. (2024). Analyzing Financial Market Trends in Cryptocurrency and Stock Prices Using CNN-LSTM Models.

Xu, X., Xu, Z., Ling, Z., Jin, Z., & Du, S. (2024). Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations. arXiv preprint arXiv:2403.02760.

Ping G, Wang S X, Zhao F, et al. Blockchain Based Reverse Logistics Data Tracking: An Innovative Approach to Enhance E-Waste Recycling Efficiency[J]. 2024.

Lu, W., Ni, C., Wang, H., Wu, J., & Zhang, C. (2024). Machine Learning-Based Automatic Fault Diagnosis Method for Operating Systems.

Zhang, Y., Xie, H., Zhuang, S., & Zhan, X. (2024). Image Processing and Optimization Using Deep Learning-Based Generative Adversarial Networks (GANs). Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 50-62.

Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility.

Zhao, F., Li, H., Niu, K., Shi, J., & Song, R. (2024). Application of deep learning-based intrusion detection system (IDS) in network anomaly traffic detection.

Wang, Z., Yan, H., Wang, Y., Xu, Z., Wang, Z., & Wu, Z. (2024). Research on autonomous robots navigation based on reinforcement learning. arXiv preprint arXiv:2407.02539.

Jiang, L., Yu, C., Wu, Z., & Wang, Y. (2024). Advanced AI framework for enhanced detection and assessment of abdominal trauma: Integrating 3D segmentation with 2D CNN and RNN models. arXiv preprint arXiv:2407.16165.

Yan, H., Wang, Z., Xu, Z., Wang, Z., Wu, Z., & Lyu, R. (2024). Research on image super-resolution reconstruction mechanism based on convolutional neural network. arXiv preprint arXiv:2407.13211.

Wang, Z. (2024, August). CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10) (pp. 143-151).

Wang, Z., Zhu, Y., He, S., Yan, H., & Zhu, Z. (2024). LLM for Sentiment Analysis in E-commerce: A Deep Dive into Customer Feedback. Applied Science and Engineering Journal for Advanced Research, 3(4), 8-13.

Yao, J., & Yuan, B. (2024). Optimization Strategies for Deep Learning Models in Natural Language Processing. Journal of Theory and Practice of Engineering Science, 4(05), 80-87.

Xu, X., Yuan, B., Song, T., & Li, S. (2023, November). Curriculum recommendations using transformer base model with infonce loss and language switching method. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 389-393). IEEE.

Xu, J., Jiang, Y., Yuan, B., Li, S., & Song, T. (2023, November). Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 384-388). IEEE.

Ren, Z. (2024). VGCN: An Enhanced Graph Convolutional Network Model for Text Classification. Journal of Industrial Engineering and Applied Science, 2(4), 110-115.

Ren, Z. (2024). Enhanced YOLOv8 Infrared Image Object Detection Method with SPD Module. Journal of Theory and Practice in Engineering and Technology, 1(2), 1–7. Retrieved from https://woodyinternational.com/index.php/jtpet/article/view/42

Ji, H., Xu, X., Su, G., Wang, J., & Wang, Y. (2024). Utilizing Machine Learning for Precise Audience Targeting in Data Science and Targeted Advertising. Academic Journal of Science and Technology, 9(2), 215-220.

Downloads

Published

2024-09-02
Loading...