A Case Study on the Recommendation Services for Customized Fashion Styles based on Artificial Intelligence

An, Hyosun; Kwon, Suehee; Park, Min Jung

doi:10.5850/jksct.2019.43.3.349

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…From the text mining results used in the initial keyword analysis we selected keywords related to intentions of AI fashion curation based on previous studies which revealed factors affecting adoption of AI service use [13,34,35,36]. Then we grouped similar keywords and named the groups corresponding to them.…”

Section: Use Intentions Resultsmentioning

confidence: 99%

“…Recently, AI, Big Data, and data mining have played increasingly important roles in fashion industry trends [6,8,35,36,49], and the introduction of AI into the fashion industry has changed strategic decision-making regarding production, sales, marketing, and even fashion design [49]. Consequently, AI adoption in fashion industry has attracted great attention among researchers.…”

Section: Discussionmentioning

confidence: 99%

“…However, Stock and Merkle (2017) [34] found that customers' expectations regarding AI devices, including their expectations regarding service quality, influence their intentions to use AI devices. In addition, other studies have identified reliability, dependability, transparency, explainability, convenience, and ease of use as critical determinants of customer satisfaction with AI curation services [8,13,35]. Choi, Cha, and Choi (2018) [36] reported that AI-based curation shopping services that offer recommendations and personalized products have higher usability and reliability ratings, leading to favorable consumer attitudes and bolstering use and purchase intentions [36].…”

Section: Perceptions Of and Intentions To Use Aimentioning

confidence: 99%

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Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

2022

KSII TIIS

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An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

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Section: Use Intentions Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Perceptions Of and Intentions To Use Aimentioning

confidence: 99%

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Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

2022

KSII TIIS

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“…Researchers have also considered user features such as facial features, body shapes, personal choice or preference, locations and wearing occasions in predicting users' fashion interests [31,[75][76][77][78]. A well-defined user profile can differentiate a more personalized or customized recommendation system from a conventional system [28,79]. Various research projects on apparel recommendation systems with personalized styling guideline and intelligent recommendation engines have been conducted based on similarity recommendation and expert advisor recommendation systems [10,58,61].…”

Section: Fashion Recommendation System (Frs) Algorithmic Models and Filtering Techniquesmentioning

confidence: 99%

“…Fashion recommendation systems (FRSs) generally provide specific recommendations to the consumer based on their browsing and previous purchase history. Social-network-based FRSs consider the user's social circle, fashion product attributes, image parsing, fashion trends, and consistency in fashion styles as important factors since they impact upon the user's purchasing decisions [28][29][30][31][32][33][34][35][36][37][38]. FRSs have the ability to reduce transaction costs for consumers and increase revenue for retailers.…”

Section: Introductionmentioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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Helping Online Fashion Customers Help Themselves: Personalised Recommender Systems

Pereira

Costa

Vieira

et al. 2023

Reinventing Fashion Retailing

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A Case Study on the Recommendation Services for Customized Fashion Styles based on Artificial Intelligence

Cited by 14 publications

References 4 publications

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

Fashion Recommendation Systems, Models and Methods: A Review

Helping Online Fashion Customers Help Themselves: Personalised Recommender Systems

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