User Experience Design Using Machine Learning: A Systematic Review (original) (raw)
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Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
HCI has become particularly interested in using machine learning (ML) to improve user experience (UX). However, some design researchers claim that there is a lack of design innovation in envisioning how ML might improve UX. We investigate this claim by analyzing 2,494 related HCI research publications. Our review confirmed a lack of research integrating UX and ML. To help span this gap, we mined our corpus to generate a topic landscape, mapping out 7 clusters of ML technical capabilities within HCI. Among them, we identified 3 under-explored clusters that design researchers can dig in and create sensitizing concepts for. To help operationalize these technical design materials, our analysis then identified value channels through which the technical capabilities can provide value for users: self, context, optimal, and utility-capability. The clusters and the value channels collectively mark starting places for envisioning new ways for ML technology to improve people's lives.
A Review on the Role of Machine Learning in Enhancing User Experience in E-commerce Applications
Machine Learning is now becoming one of the leading topics in the technology world. So it comes to no one's surprise that leading e-commerce companies would look into machine learning to incorporate into their commerce application. User Experience is an important aspect for smooth business operation since customer satisfaction is the key to greater productivity. To automise several operations within the ecommerce application has proven advantageous and profitable for most business operators. This paper explores and reviews the various applications of machine learning in ecommerce as a tool to enhance the user experience.
HOW AI-POWERED TOOLS ARE TRANSFORMING USER RESEARCH, PROTOTYPING, OR PERSONALIZATION IN UI/UX DESIGN
IAEME PUBLICATION, 2020
In today’s AI powered world, no domain goes untouched by the AI tools and techniques. The world has risen on huge data and the unmatched computational speed of machines launching every single step. This article is ready with the transformative impact of AI-powered tools on user research, prototyping, and personalization in the realm of UI/UX design. As artificial intelligence continues to advance, its integration into design process is reshaping traditional methodologies and enhancing the user experience. Through a comprehensive review of existing literature, case studies, and expert insights, this paper explores the various ways in which AI technologies are revolutionizing key aspects of UI/UX design. Specifically, it investigates how AI facilitates user research efficiently by automating data analysis, identifying patterns, and predicting user behaviour. Furthermore, the paper delves into the role of AI in accelerating the prototyping process through generative design algorithms, enabling designers to explore a multitude of design variations rapidly. Additionally, it discusses the implications of AI-driven personalization in user experience based on individual preferences and behaviors, thereby enhancing engagement and satisfaction. By elucidating these advancements, this paper aims to provide a deeper understanding of the evolving landscape of UI/UX design in the context of AI innovation. This in-depth discussion offers insights for designers, researchers, and industry practitioners alike.
Machine Learning for Designers
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Inputs In addition to physical forms of input, machine learning allows designers to discover implicit patterns within numerous facets of a user’s behavior. These patterns carry inherent meanings, which can be learned from and acted upon, even if the user is not expressly aware of having communicated them. In this sense, these implicit patterns can be thought of as input modalities that, in practice, serve a very similar purpose to the more tangible input modes described above. Mining behavioral patterns through machine learning can help designers to better understand users and serve their needs. At the 32 | Machine Learning for Designers same time, these patterns can also help designers to understand the products or services they offer as well as the implicit relationships between these offerings. Behavioral patterns can be mined in rela‐ tion to an individual user or aggregated from the collective behav‐ iors of numerous users. One form of pattern mining that can be useful in servi...
Applied Computer Systems
Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.
Machine learning as a design material: a curated collection of exemplars for visual interaction
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Although machine learning is not a new phenomenon, it has truly entered the spotlight in recent years. With growing expectations, we see a shift in focus from performance tuning to awareness of meaningful interaction and purpose. Interaction design and UX research is currently in a position to provide important and necessary knowledge contributions to the development of machine learning systems. Machine learning can be viewed as a design material that is arguably more unpredictable, emergent, and “alive” than traditional ones. These characteristics suggest practice-based work along the lines of research-through-design as a promising approach for machine learning system development research. Design researchers using a research-through-design approach agree that a created artefact carries knowledge, but there is no consensus on how such knowledge is best articulated and transferred within academic discourse. Knowledge contributions need to be abstracted from the particular to a higher...
IRJET, 2020
Machine Learning is now becoming one of the leading topics in the technology world. So it comes to no one's surprise that leading e-commerce companies would look into machine learning to incorporate into their commerce application. User Experience is an important aspect for smooth business operation since customer satisfaction is the key to greater productivity. To automise several operations within the ecommerce application has proven advantageous and profitable for most business operators. This paper explores and reviews the various applications of machine learning in e-commerce as a tool to enhance the user experience.
Reimagining the Goals and Methods of UX for ML/AI
This position paper for the "Designing the User Experience of Machine Learning Systems " symposium challenges UX conventions and proposes new approaches for Machine Learning (ML) and Artificial Intelligence (AI). Through live demos and a presentation, I'll discuss how designers can reimagine the goals and focus of UX for the unique potentials of ML/AI. Using animistic design as an example, I'll propose how using simple intelligence, machine learning, and autonomous personalities can allow the designer to shift from crafting task oriented experiences for users, to building evolving, diverse, autonomous ecologies that support collaborative exploration and creativity for machine and human participants alike.
Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)
International Journal of Machine Learning and Computing, 2020
Industries use various platforms to receive feedback from users of their products. In this paper, there is an overview of the potentials of using natural language processing system (NLP) in classifying the quality of user experience. The user experience is captured using google form. To test the efficacy of the platform, sentiments of users were analysed using hotels.ng as the source of data. The natural processing of electronic word of mouth (e-WOM) can be applied to any feedback platforms to classify and predict customers' sentiments and provide a veritable opportunity for companies to capture the quality of users' experiences and improve service delivery. The feature or sentiments extraction was done using opinion mining and data cleaning tools on heterogeneous data sources to judge the decision-making process of users. Using charts and correlations, with an average performance level of the willingness to recommend and degree of review helpfulness, the platform showed that the Quality of User Experience (QoUE) of the customers are 7.31 and 7.03 respectively. Finally, an improved logistic regression classifier was developed to test, train and classify the user experiences. Comparing the improved logistic regression classifier with standard logistic regression classifier shows that the training accuracy of the proposed improved logistic regression gave 97.67% as against the standard logistic regression which had accuracy of 86.01%
Interactive Machine Learning for End-User Innovation
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User interaction with intelligent systems need not be limited to interaction where pre-trained software has intelligence “baked in.” End-user training, including interactive machine learning (IML) approaches, can enable users to create and customise systems themselves. We propose that the user experience of these users is worth considering. Furthermore, the user experience of system developers—people who may train and configure both learning algorithms and their user interfaces—also deserves attention. We additionally propose that IML can improve user experiences by supporting user-centred design processes, and that there is a further role for user-centred design in improving interactive and classical machine learning systems. We are developing this approach and embodying it through the design of a new User Innovation Toolkit, in the context of the European Commission-funded project RAPID-MIX.