A Framework for Adaptive User Interface Generation based on User Behavioural Patterns

Rathnayake, Nilanka; Meedeniya, Dulani; Perera, Indika; Welivita, Anuradha

doi:10.1109/mercon.2019.8818825

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…By far the most popular are product recommendations and special offers [42]. Less popular, due to the complexity of implementation, is comprehensive customization of the entire layout of websites, although efforts are being made in this direction [22], [23]. The primary goal of personalization in e-commerce is un-…”

Section: Literature Review a Personalization In E-commercementioning

confidence: 99%

One Size Does Not Fit All: Multivariant User Interface Personalization in E-Commerce

Wasilewski,

Kolaczek

2024

IEEE Access

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One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to ecommerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI.

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Section: Literature Review a Personalization In E-commercementioning

confidence: 99%

One Size Does Not Fit All: Multivariant User Interface Personalization in E-Commerce

Wasilewski,

Kolaczek

2024

IEEE Access

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“…e researchers have used various approaches such as model-driven [34,35], userdriven/user-centered design [36,37], context-driven [38], ontology-based [39,40], and machine learning-based [41,42] to overcome the usability and UX issues of complex user interfaces of the interactive systems and proposed the concept of adaptive user interfaces to fulfil the requirements of each individual according to their abilities, preference, ages, mental mode, skills, and education. Adaptive user interfaces have been used in many domains, including operating systems, Web-based applications, and smartphone-based apps [43].…”

Section: Tvosmentioning

confidence: 99%

“…Besides, it enhances the interaction between the user and the system. e adaptive user interface adjusted the layouts, elements, components, font size, color, contrast, theme, and app arrangements according to the user's requirements, abilities, experiences, and context [41]. e adaptive user interfaces reduce the usability issues and challenges of complex and cluttered user interfaces and improve the users' performance [34,41].…”

Section: Tvosmentioning

confidence: 99%

“…Various adaptive user interfaces have been developed for senior citizens, children, drivers, blind and visually impaired person, and domain-specific applications, such as e-commerce applications (eBay, Amazon), health applications, gamification applications, e-learning applications, and entertainment applications [32,41,[44][45][46][47][48][49][50][51][52][53] for various computing platforms, i.e., smart TVs, smartphones, and computers. However, the intelligent TV UIs are in the infant stage.…”

Section: Tvosmentioning

confidence: 99%

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Perspectives on the Design, Challenges, and Evaluation of Smart TV User Interfaces

Khan

Khusro

Alam

et al. 2022

Scientific Programming

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The user interface (UI) is a primary source of interaction with a device. Since the introduction of graphical user interface (GUI), software engineers and designers have been trying to make user-friendly UIs for various computing devices, including smartphones, tablets, and computers. The modern smart TV also comes with built-in operating systems. However, little attention has been given to this prominent entertainment device, i.e., smart TV. The technological advancement and proliferation of smart TV enabled the manufacturer to provide rich functionalities and features; however, this richness resulted in more clutter and attention-demanding interfaces. Besides, smart TV is a lean-back supporting device having a diverse range of users. Therefore, smart TV’s usability and user experience (UX) are questionable due to diverse user interests and limited features of traditional remote controls. This study aimed to discuss and critically analyze the features and functionalities of the existing well-known smart TV UIs of various operating systems in the context of usability, cognition, and UX. Moreover, this study highlights the issues and challenges in the current smart TV UIs and recommends some research opportunities to cope with the smart TV UIs. This study further reports and validates some overlooked factors affecting smart TV UIs and UX. A subjective study and usability tests from diverse users are presented to validate these factors. The study concludes that a one-size-fits-all UI design is unsuitable for shared devices, i.e., smart TV. This study further recommends a personalized adaptive UI, which may enhance the learnability and UXs of the smart TV viewers.

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“…A user behaviour analysed modified adaptive user interface (AUI) was reported by Rathnayake et al (2019) for supplementing common software platforms. Their research showed the requirement for a generic software infrastructure for AUI by conducting studies on customised web interfaces using machine learning.…”

Section: Related Workmentioning

confidence: 99%

Cross-cultural usability evaluation of AI-based adaptive user interface for mobile applications

Miraz

Ali²

2022

Acta Sci Technol

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With the widespread purchase of mobile communication devices and their extensive usage in every aspect of life, allied with global mobility and market penetration – a more culturally universally adaptable interface has become a priority. This pliable interface must conform continuously with the changing abilities of the end user and the person’s culture, irrespective of the prevailing ambient culture. The information required to customise this interface must be derived from the user’s actual digital footprint and not on their feedback. This treatise presents the usability evaluation results of a culturally inclusive and ubiquitous mobile learning (M-Learning) platform (‘Mobile Academy’), with an AI-based adaptive user interface which takes the snapshot of the installed apps on a smartphone as input, predicts the user’s cultural affiliation as well as the language preference and thus offer a culturally customised user interface as the output. The proof of concept (PoC) prototype has been developed based on the CIAUI (Culturally Inclusive Adaptive User Interface) framework, using plasticity of user interface techniques. This approach was taken to test the affordability of developing inclusive applications, considering the ever growing large global culturally diverse user base. Usability evaluation was then conducted and the results carefully analysed. The results indicated that the PoC exhibited enhanced cross-cultural usability and affordability of such techniques. The evaluation results of the PoC also advocates in favour of the user’s cultural profiling based on the mobile usage data, particularly a single snapshot of installed apps. The research provides direction for future research and application development.

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A Framework for Adaptive User Interface Generation based on User Behavioural Patterns

Cited by 13 publications

References 11 publications

One Size Does Not Fit All: Multivariant User Interface Personalization in E-Commerce

One Size Does Not Fit All: Multivariant User Interface Personalization in E-Commerce

Perspectives on the Design, Challenges, and Evaluation of Smart TV User Interfaces

Cross-cultural usability evaluation of AI-based adaptive user interface for mobile applications

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