Consumer-Driven Usability Test of Mobile Application for Tea Recommendation Service

Lee, Jisun; Kang, Seungwoo

doi:10.3390/app9193961

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Both of these studies utilised statistical analysis methods, and this single form of analysis may have led to the poor comparability of their data. Lee et al [8] studied a recommendation service of blended tea in conjunction with a food recommendation system and verified the feasibility of blended tea recommendations after final consumer acceptance tests. In recent years, with the advancement and application of artificial intelligence technology, deep learning has been incorporated into tea quality research.…”

Section: Introductionmentioning

confidence: 99%

“…These studies show that link prediction algorithms have been extensively investigated in various domains yet still need to be explored with regard to tea. Furthermore, the aforementioned studies [5][6][7][8][9] mainly focused on the substance composition of specific tea types, and studies on the associated relationships between tea and people can still be fully explored.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Tea Varieties’ “Suitable for People” Relationship: Based on the InteractE-SE+GCN Model

Huang,

Wu,

Wang

et al. 2023

Agriculture

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This study proposes an improved link prediction model for predicting the “suitable for people” relationship within the knowledge graph of tea. The relationships between various types of tea and suitable target groups have yet to be fully explored, and the existing InteractE model still does not adequately capture a portion of the complex information around the interactions between entities and relationships. In this study, we integrate SENet into the feature layer of the InteractE model to enhance the capturing of helpful information in the feature channels. Additionally, the GCN layer is employed as the encoder, and the SENet-integrated InteractE model is used as the decoder to further capture the neighbour node information in the knowledge graph. Furthermore, our proposed improved model demonstrates significant improvements compared to several standard models, including the original model from public datasets (WN18RR, Kinship). Finally, we construct a tea dataset comprising 6698 records, including 330 types of tea and 29 relationship types. We predict the “suitable for people” relationship in the tea dataset through transfer learning. When comparing our model with the original model, we observed an improvement of 1.4% in H@10 for the WN18RR dataset, a 7.6% improvement in H@1 for the Kinship dataset, and a 5.2% improvement in MRR. Regarding the tea dataset, we achieved a 4.1% increase in H@3 and a 2.5% increase in H@10. This study will help to fully exploit the value potential of tea varieties and provide a reference for studies assessing healthy tea drinking.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Tea Varieties’ “Suitable for People” Relationship: Based on the InteractE-SE+GCN Model

Huang,

Wu,

Wang

et al. 2023

Agriculture

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Artificial Perception of the Beverages: An In-Depth Review of the Tea Sample

2021

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India is the second-largest tea producer and consumer in the world after China. In 2017, the Indian tea market size accounted for 130 billion Indian rupees. An estimated global tea market size was at USD 13.31 billion in 2019, and the expected compound annual growth rate is 5.5% up to the year 2025. India can grab worth tea market size globally by making market strategies with AI and ML-based demonstrations for the unique identity of tea flavor. Conventional instruments available are not handy, time-consuming and require a skilled person to operate. The tea attributes should be digitally recognizable before purchase from the consumer's perspective, significantly enlarging the tea market circle. In the paper, the comprehensive review about an artificial perception of tea has been briefly discussed. Three major attributes of the tea sample, its taste, smell, and color, are under consideration. With the help of various sensors, the attributes of liquefied tea samples had got converted into their digital signature. By analyzing the correlation of them with the pattern recognition, their classification had done. The electronic feature fusion of tea liquor attributes may cause handling issues with the formation of redundant data. So this paper explains the method and guidelines of an application of specific filters which remove the redundant data. The constructive sample data can establish the decision matrix for correlation. With the established decision matrix, précised test prediction can be achieved for the tea sample based on correlation and regression. The limitations and glitches of the conventional instruments for an artificial perception have been discussed in-depth for possible improvement. The paper ends with a bibliometric analysis of the topic "artificial taste perception of tea," which had derived from the standard repository of Web of Science. The bibliometric analysis is very useful to showcase the current research trends in the artificial taste perception of tea.

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Comparison of Usability and Prefrontal Cortex Activity of Cognitive-Motor Training Programs using Sensor-Based Interactive Systems

Jung

Lee

2022

Phys Ther Rehabil Sci

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Objective: Cognitive-motor trainings had a positive impact on cognitive function and dual-task trainings led to improvements of global cognitive function. The brain activity of the prefrontal cortex (PFC) is another indicator that can infer cognitive function. This study aims to confirm the usability of the interactive system cognitive-motor training program and the changes in the prefrontal cortex through training. Design: Cross-sectional studyMethods: In this study, two cognitive tasks were randomly applied to 20 adults as cognitive-motor training using an interactive system, and the same task was performed using the original method. During all tasks, the brain activity of the prefrontal cortex was measured by the change in oxyhemoglobin (HbO) in real-time using Functional Near-Infrastructure. After performing the tasks, the usability of the developed interactive system was evaluated by a usability questionnaire which consists of five items, and each item consists of a 7-point Likert scale that responds from 1 point to 7 points. Results: The HbO levels were increased during cognitive task performance than at the resting phase. And evaluating the usefulness of the interactive system, a questionnaire result showed that it would be useful for cognitive-motor programs. Conclusions: The cognitive-motor training using the interactive system increased the activity of the prefrontal cortex, and the developed wearable sensor-based interactive system confirmed its usefulness.

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Consumer-Driven Usability Test of Mobile Application for Tea Recommendation Service

Cited by 3 publications

References 22 publications

Prediction of Tea Varieties’ “Suitable for People” Relationship: Based on the InteractE-SE+GCN Model

Prediction of Tea Varieties’ “Suitable for People” Relationship: Based on the InteractE-SE+GCN Model

Artificial Perception of the Beverages: An In-Depth Review of the Tea Sample

Comparison of Usability and Prefrontal Cortex Activity of Cognitive-Motor Training Programs using Sensor-Based Interactive Systems

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