AI-Based UI Adaptation for Hybrid Cloud Customer Portals
Keywords:
AI-powered UI personalization,Customer self-service automation,Hybrid cloud user experience,Adaptive interface design,Intelligent customer portals,Context-aware personalization,Cloud-native self-service platforms,Machine learning UX optimization,Personalized digital support systems,Enterprise hybrid cloud UX AI.Abstract
User interface personalization and user experience enrichment have been explored separately in different contexts and domains, but in customer self-service interactions deployment in hybrid cloud environments there is no evidence of such merging for enhancing customer experience. Therefore, the main contribution of this article is the evidence-based design and development of an AI-powered user-interface-personalization component predominantly for customer self-service interactions in hybrid cloud environments. A process-oriented ontology of user-interface personalization is defined and the specific formulation of a user-interface-personalization function expresses how and why AI services and systems relevant for customer self-service interactions are personalized.
Actionable items for business process management and development, customer experience management, and marketing communication are derived from the interplay of personalization functions as a two-way customer-interface-network relationship that support user-interface personalization towards specific business objectivesevolving across three stageshealth optimisation, process enabler, and process enhancer. These stages define four user-interface-personalization taxonomies with customer self-service context—chatbots, virtual assistants, machine-learning customer serviceanswers components—and the related four generated business-objective classifications concentration, engagement, knowledge-generation, and innovation-enhancement enrichmentextendable to hybrid-cloud-contextualised user experience design and interaction design for any specific user-interface personalization function.
References
1. Abrahão, S., Insfran, E., Sluÿters, A., & Vanderdonckt, J. (2021). Model-based intelligent user interface adaptation: Challenges and future directions. Software and Systems Modeling, 20(5), 1335–1349.
2. Planas, E., Daniel, G., Brambilla, M., & Cabot, J. (2021). Towards a model-driven approach for multiexperience AI-based user interfaces. Software and Systems Modeling, 20(4), 997–1009.
3. Kawsar, F., Dey, A. K., & Ahmed, T. (2021). Human-centered artificial intelligence for adaptive user interfaces: A survey. ACM Computing Surveys, 54(9), 1–36.
4. Oladosu, S. A., Ige, A. B., Ike, C. C., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2022). Revolutionizing data center security: Conceptualizing a unified security framework for hybrid and multi-cloud data centers. Open Access Research Journal of Science and Technology, 5(2), 086-076.
5. Xu, W., Dainoff, M. J., Ge, L., & Gao, Z. (2021). From human–computer interaction to human–AI interaction: New challenges and opportunities for adaptive interfaces. International Journal of Human–Computer Interaction, 37(16), 1527–1544.
6. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
7. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2021). Guidelines for human-AI interaction. Communications of the ACM, 64(4), 72–80.
8. Feitosa, D., Silva, A. R., & Ribeiro, P. (2021). Adaptive web user interfaces based on user behavior and context awareness. Journal of Web Engineering, 20(6), 1793–1818.
9. Brambilla, M., Cabot, J., & Wimmer, M. (2021). Model-driven engineering for AI-enabled web applications. IEEE Software, 38(6), 78–85.
10. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
11. Benyon, D., Mival, O., & Ahsan, R. (2021). Designing adaptive and personalized user experiences in intelligent interactive systems. Interacting with Computers, 33(5), 479–494.
12. Chen, L., Xu, D., & Wang, H. (2022). Intelligent personalization for cloud-based web applications using deep learning. Future Generation Computer Systems, 126, 212–224.
13. Zhang, Y., Liu, X., & Chen, J. (2022). Artificial intelligence for user experience optimization in cloud computing environments. Journal of Systems and Software, 188, 111248.
14. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
15. Alshammari, M., & Hussain, F. K. (2022). Context-aware recommendation techniques for adaptive web interfaces: A systematic review. Expert Systems with Applications, 197, 116689.
16. Sarker, I. H. (2022). AI-based personalization: Techniques and applications for intelligent systems. Journal of Big Data, 9(1), 30.
17. Wang, S., Zhang, Q., & Buyya, R. (2022). Hybrid cloud computing: Architecture, resource management, and applications. Future Generation Computer Systems, 129, 1–18.
18. Silva, E., Rodrigues, C., & Martins, B. (2022). Adaptive interaction techniques for intelligent web applications: A review. Information, 13(10), 486.
19. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
20. Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024). AdaptUI: A framework for the development of adaptive user interfaces in smart product-service systems. User Modeling and User-Adapted Interaction, 34, 1929–1980.
21. Balla, D., & Kovács, T. (2024). Towards an AI-driven user interface design for web applications. Procedia Computer Science, 237, 179–186.
22. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
23. Kaczmarek, P., & Nowak, M. (2024). Recommendations user interface in web-based e-commerce systems. Procedia Computer Science, 246, 2874–2881.
24. Gaspar-Figueiredo, D., Fernández-Diego, M., Nuredini, R., Abrahão, S., & Insfrán, E. (2024). Reinforcement learning-based framework for the intelligent adaptation of user interfaces. arXiv.
25. Kolla, T. (2024). Intelligent Discovery and Governance of Healthcare Data Assets Through AI-Powered Catalog Architectures. International Journal of Emerging Trends in Engineering and Management Research, 9(4), 16083.
26. Bieniek, J., Rahouti, M., & Verma, D. C. (2024). Generative AI in multimodal user interfaces: Trends, challenges, and cross-platform adaptability. arXiv.
27. Chen, D., Youssef, A., Pendse, R., Schleife, A., Clark, B. K., Hamann, H., He, J., Laino, T., Varshney, L., Wang, Y., Sil, A., Jabbarvand, R., Xu, T., Kindratenko, V., Costa, C., Adve, S., Mendis, C., Zhang, M., Núñez-Corrales, S., ... Puri, R. (2024). Transforming the hybrid cloud for emerging AI workloads. arXiv.
28. Todi, K., Bailly, G., Leiva, L. A., & Oulasvirta, A. (2021). Adapting user interfaces with model-based reinforcement learning. International Journal of Human–Computer Studies, 151, 102630.
29. Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
30. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160.
31. Buyya, R., Srirama, S. N., Casale, G., Calheiros, R. N., Dias de Assunção, M., & others. (2021). A manifesto for future generation cloud computing: Research directions for the next decade. ACM Computing Surveys, 54(5), 1–38.
32. Xu, X., Zhang, H., Gao, S., Yu, L., & He, W. (2021). Adaptive service composition for cloud applications using artificial intelligence. Future Generation Computer Systems, 115, 269–281.
33. Alzahrani, B., Mahmood, A., Lloret, J., & Song, H. (2021). AI-enabled intelligent cloud computing: State of the art and future directions. Journal of Network and Computer Applications, 189, 103134.
34. Chen, L., Chen, P., & Lin, Z. (2022). Artificial intelligence in intelligent systems: A systematic literature review. Expert Systems with Applications, 188, 116054.
35. Hussain, A., Mkpojiogu, E. O. C., & Abdullah, N. (2022). User experience design for intelligent web applications: Recent advances and future trends. International Journal of Human–Computer Interaction, 38(12), 1125–1141.
36. Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
37. Brambilla, M., Cabot, J., & Wimmer, M. (2022). Engineering intelligent web applications with model-driven techniques. IEEE Software, 39(4), 54–62.
38. Li, Y., Xu, L. D., Zhao, S., & Wang, X. (2022). Artificial intelligence for cloud computing: A survey. IEEE Transactions on Industrial Informatics, 18(4), 2784–2797.
39. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
40. Sarker, I. H. (2022). Smart user-centric artificial intelligence for next-generation intelligent systems. Journal of Big Data, 9(1), 52.
41. Jansen, S., Brinkkemper, S., & Cusumano, M. A. (2022). Software ecosystems for cloud-based digital platforms. Information and Software Technology, 145, 106816.
42. Wang, S., Zhang, Q., & Buyya, R. (2022). Resource management in hybrid cloud computing environments: A survey. ACM Computing Surveys, 55(6), 1–36.
43. Zhang, Y., Liu, X., & Wang, H. (2023). Deep learning-enabled adaptive user interfaces for intelligent web systems. Information and Software Technology, 154, 107089.
44. Peddi, R. K. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589-8601.
45. Chen, M., Mao, S., & Liu, Y. (2023). Artificial intelligence for cloud computing and intelligent services: Recent advances. IEEE Network, 37(2), 52–59.
46. Kumar, N., Sharma, P., & Singh, R. (2023). Context-aware personalization in web applications using machine learning techniques. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10435–10449.
47. Ramesh, A., Gupta, V., & Chandra, S. (2023). Hybrid cloud architecture for scalable enterprise applications: A comprehensive review. Future Internet, 15(7), 232.
48. Huang, Y., Kanij, T., Madugalla, A., Mahajan, S., Arora, C., & Grundy, J. (2024). Unlocking adaptive user experience with generative AI. arXiv.
49. Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011-1027.
50. Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024). AdaptUI: A framework for the development of adaptive user interfaces in smart product-service systems. User Modeling and User-Adapted Interaction, 34, 1929–1980.
51. Sun, Q., Xue, Y., & Song, Z. (2024). Adaptive user interface generation through reinforcement learning: A data-driven approach to personalization and optimization. arXiv.
52. Chen, D., Youssef, A., Pendse, R., Schleife, A., Clark, B. K., Hamann, H., He, J., Laino, T., Varshney, L., Wang, Y., Sil, A., Jabbarvand, R., Xu, T., Kindratenko, V., Costa, C., Adve, S., Mendis, C., Zhang, M., Núñez-Corrales, S., … Puri, R. (2024). Transforming the hybrid cloud for emerging AI workloads. arXiv.
53. Adadi, A., & Berrada, M. (2023). Explainable artificial intelligence (XAI): Concepts, applications, research challenges, and future directions. Expert Systems with Applications, 216, 119192.
54. Zhou, J., Chen, F., Berry, M., & Zhang, Y. (2023). Human-centered artificial intelligence for adaptive interactive systems: A survey. ACM Computing Surveys, 56(2), 1–38.
55. Mangala, N. (2024). Leveraging Microsoft Fabric lakehouse as an AI-ready data platform for enterprise analytics. Journal of Information Systems Engineering and Management.
56. Li, X., Wang, H., Zhang, Q., & Chen, Y. (2023). Intelligent personalization for cloud-native web applications using deep learning. Future Generation Computer Systems, 141, 220–233.
57. Buyya, R., Srirama, S. N., & Casale, G. (2023). Next-generation hybrid cloud computing: Architectures, technologies, and applications. IEEE Cloud Computing, 10(3), 20–31.
58. Ghosh, A., Banerjee, S., & Roy, P. (2023). Artificial intelligence techniques for personalized digital customer experience: A review. Journal of Retailing and Consumer Services, 72, 103256.
59. Nguyen, T. T., Pham, T. H., & Le, D. H. (2023). Context-aware adaptive web interfaces using machine learning. Journal of Web Engineering, 22(5), 1231–1254.
60. Zhang, L., Xu, Y., & Wang, J. (2023). Intelligent cloud resource optimization using reinforcement learning. Future Generation Computer Systems, 145, 430–442.
61. Huang, Y., Kanij, T., Arora, C., Mahajan, S., & Grundy, J. (2024). Unlocking adaptive user experience with generative AI. arXiv.
62. Gaspar-Figueiredo, D., Fernández-Diego, M., Nuredini, R., Abrahão, S., & Insfrán, E. (2024). Reinforcement learning-based framework for intelligent adaptation of user interfaces. arXiv.
63. Sun, Q., Xue, Y., & Song, Z. (2024). Adaptive user interface generation through reinforcement learning: A data-driven approach to personalization and optimization. arXiv.
64. Balla, D., & Kovács, T. (2024). Towards an AI-driven user interface design for web applications. Procedia Computer Science, 237, 179–186.
65. Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024). AdaptUI: A framework for the development of adaptive user interfaces in smart product-service systems. User Modeling and User-Adapted Interaction, 34, 1929–1980.
66. Kaczmarek, P., & Nowak, M. (2024). Recommendations user interface in web-based e-commerce systems. Procedia Computer Science, 246, 2874–2881.
67. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
68. Chen, D., Youssef, A., Pendse, R., Schleife, A., Clark, B. K., Hamann, H., He, J., Laino, T., Varshney, L., Wang, Y., Sil, A., Jabbarvand, R., Xu, T., Kindratenko, V., Costa, C., Adve, S., Mendis, C., Zhang, M., Núñez-Corrales, S., ... Puri, R. (2024). Transforming the hybrid cloud for emerging AI workloads. arXiv.
69. Rahouti, M., Bieniek, J., & Verma, D. C. (2024). Generative AI for multimodal user interfaces: Opportunities and challenges. arXiv.
70. Zhang, H., Li, Y., & Wang, S. (2024). AI-powered adaptive interfaces for enterprise cloud applications. IEEE Access, 12, 87421–87437.
71. Kumar, N., Sharma, P., & Singh, R. (2024). Intelligent customer experience management using artificial intelligence in cloud platforms. Journal of Ambient Intelligence and Humanized Computing, 15(2), 1875–1890.
72. Silva, A. R., Rodrigues, C., & Martins, B. (2024). Personalized intelligent web systems using user behavior analytics. Information, 15(3), 148.
73. Chen, M., Mao, S., & Liu, Y. (2024). Artificial intelligence for cloud computing and intelligent services: Recent advances and future opportunities. IEEE Network, 38(1), 66–74.
74. Wang, S., Zhang, Q., & Buyya, R. (2024). Hybrid cloud computing for AI-driven enterprise applications: Trends, challenges, and future research directions. Future Generation Computer Systems, 154, 118–133.
