Evaluation of User-Subjective Web Interface Similarity with Kansei Engineering-Based ANN

Bakaev, Maxim; Khvorostov, Vladimir; Heil, Sebastian; Gaedke, Martin

doi:10.1109/rew.2017.13

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…The existing literature [35] also endorsed the approach. Although the generalization ability of the model might be affected by the mood, emotion, and aesthetic preference of the respondents [26], the optimization process in this study still made the model present a small RMSE. After training the model, the saved parameters such as the radial basis kernel parameter and the weights of each support vector helped to continuously test the rationality of the design schemes in progress by inputting the evaluation matrix on the 13 design factors, which could provide an effective simulation method for automobile enterprises in the sustainable development of VMUAVs and the avoidance of the blind following problem.…”

Section: Discussion and Limitationsmentioning

confidence: 90%

“…In addition, it was found that the users' images had a significant influence (p < 0.001) on their preference of a form. The methods of Kansei Engineering, such as factor analysis and ANN, were helpful in constructing models to reflect the relationships between design elements and people's affection [26,27]. This literature supplied a measurement basis for the approval degree of VMUAVs, but the method to deal with the nonlinear relationship among the subjective data was not fully presented.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

Yang

Huo²,

Jia³

et al. 2022

Sustainability

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In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R2 = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.

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Section: Discussion and Limitationsmentioning

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

Yang

Huo²,

Jia³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Unlike the vast majority of papers, [21] has a detailed description and availability of a data set and easily reproducible experiments. We can see the use of Artificial Neural Networks in papers [23], [24]. The paper [23] uses readily available metrics in ANN to assess web page similarity without employing actual users.…”

Section: Web Page Segmentationmentioning

confidence: 99%

“…We can see the use of Artificial Neural Networks in papers [23], [24]. The paper [23] uses readily available metrics in ANN to assess web page similarity without employing actual users. More detailed experiments and approaches that combine visual similarity and neural networks can be seen in [24].…”

Section: Web Page Segmentationmentioning

confidence: 99%

Methods for Automatic Web Page Layout Testing and Analysis: A Review

et al. 2023

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Methods for automatic analysis of user interfaces are essential for a wide range of applications in computer science and software engineering. These methods are used in software security, document archiving, human-computer interaction, software engineering, and data science. Even though these methods are essential, no single research systematically lists most of the methods and their characteristics. This paper aims to give an overview of different solutions and their applications in the separate processes of automatic analysis of user interfaces. The main focus is on the techniques that analyze web page layouts and web page structure. Web pages' style, type of content, and even structure constantly (often drastically) change, as do methods that analyze them. The fact that most methods use very different datasets and web pages of various complexities are some of the reasons that the direct comparison of methods is difficult, if not impossible. Another fact is that the vast applications of methods practically solve similar problems. With these facts in mind, in the paper, we surveyed relevant scientific articles, categorized them, and provided an overview of how these methods have developed over time.

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“…More recently, artificial neural networks (ANNs) were applied in UX classification tasks. In particular, in the study in [30] the concept of Website similarity as perceived by users was explored with the goal of facilitating the reuse of good Web designs. The authors concluded that ANN models with user impression-related inputs had a greater effect on user-assessed similarity of Websites than those with user interface intrinsic inputs.…”

Section: Evaluation Factors and Artificial Intelligencementioning

confidence: 99%

User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

et al. 2019

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Business platform models frequently require continuous adaptation and agility to allow new experiences to be created and delivered to customers. To understand user behavior in online systems, researchers have taken advantage of a combination of traditional and recently developed analysis techniques. Earlier studies have shown that user behavior monitoring data, as obtained by mouse tracking, can be utilized to improve user experience (UX). Many mouse-tracking solutions exist; however, the vast majority is proprietary, and open-source packages do not provide the resources and data needed to support UX research. Thus, this paper presents: 1) the development of an interaction monitoring application titled Artificial Intelligence and Mouse Tracking-based User eXperience Tool (AIMT-UXT); 2) the validation of the tool in a case study conducted on the Website of the Brazilian Federal Revenue Service (BFR); 3) the definition of a new relationship pattern of variables that determine user behavior; 4) the construction of a fuzzy inference system for measuring user performance using the defined variables and the data captured in the case study; and 5) the application of a clustering algorithm to complement the analysis. A comparison of the results of the applied quantitative methodologies indicates that the developed framework was able to infer UX scores similar to those reported by users in questionnaires.

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Evaluation of User-Subjective Web Interface Similarity with Kansei Engineering-Based ANN

Cited by 5 publications

References 14 publications

Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

Methods for Automatic Web Page Layout Testing and Analysis: A Review

User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

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