User Experience Design Using Machine Learning: A Systematic Review

Abdallah M. H. Abbas

doi:10.1109/ACCESS.2022.3173289

2022-01-01

Abdallah M. H. Abbas

Khairil Imran Ghauth *

Choo‐Yee Ting

DOI：10.1109/ACCESS.2022.3173289

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摘要

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User experience (UX) is the key to increased productivity by enhancing the usability and interactivity of the product. Machine learning (ML) solutions have raised user and academic awareness of technical innovation. As a result, ML is becoming increasingly popular to improve the quality of UX. Several investigations have highlighted a potential lack of studies on the overall challenges and recommendations for UX using ML. Therefore, more attention should be paid to ML's existence and potential applications across various applications to get the most out of ML techniques to improve the UX design process. To this objective, a systematic review of the literature was performed as to determine the challenges faced by UX designers when incorporating ML in their design process. Recommendations that help UX designers incorporate ML into UX design will be highlighted. Furthermore, the PRISMA approach is used (a process that has been established in the literature), to restrict the chance of bias at the selection stage. Relevant articles in the following four databases were searched: IEEE Xplore, Scopus, Web of Science, and ACM. The findings revealed that the number of publications on issues linked to UX with ML had advanced exponentially. This review highlights the challenges, recommendations, tools, algorithms, techniques and datasets used in different studies. In addition, suggestions are given for future investigations.

Keyword:

Machine learning

Databases

User experience

Libraries

Systematics

Metadata

Graphical user interfaces

User experience

experience design

machine learning

HCI