Extending the Consumer Style Inventory to Define Consumer Typologies for Secondhand Clothing Consumption in Poland

Ryding, Daniella; Cláudia,; Rudawska, Edyta; Vignali, Gianpaolo

doi:10.35808/ersj/1833

Cited by 2 publications

(2 citation statements)

References 22 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 5) It can handle the problem of multiple outputs. (6) It is insensitive to the missing values. (7) It can process irrelevant feature data.…”

Section: Design Of Clothing Recommendation Model Based Onmentioning

confidence: 99%

See 1 more Smart Citation

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.

show abstract

“…( 5) It can handle the problem of multiple outputs. (6) It is insensitive to the missing values. (7) It can process irrelevant feature data.…”

Section: Design Of Clothing Recommendation Model Based Onmentioning

confidence: 99%

“…According to independent designer brands in different regions, clothing can also be divided into young playful, avant-garde, modern and simple, national retro, elegant, and romantic styles [ 5 ]. Thereby, the division of clothing style mainly depends on personal subjective judgment without unified definition standard [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Consumer Style Inventory (CSI) Revisited: The case of Millennial Decision-making Style in a Developing Market

Oniku,

Akeke,

Akinwale

2024

Rev.Humanismo.Soc

View full text Add to dashboard Cite

This research study explores the buying styles of young consumers, especially the millennials (Gen Y, and Gen Z), whose idiosyncrasies and consumption peculiarities are quite different from previous generations. This present study further expanded on the eight constructs of the consumer style inventory (CSI) that were conceived by Sproles & Kendall, developing six additional constructs that helped define the younger consumers’ decision-making style. The sample population for this study was made up of 125 respondents (young consumers), who were selected randomly across all 20 local governments from Lagos State in Nigeria. The factor analyses through varimax rotation, the latent root criterion (eigenvalue =1), the scree plot test and the percentage of variance methodologies were conducted to determine the number of constructs that are significant to retain among the variables. The findings revealed that the new CSI constructs developed in this study (sexiness, trendiness, global branding, smartness, socialization and entertainment) are strong and significant within the young consumers’ styles of decision-making. The six (6) constructs developed reveal that the young consumers’ purchasing styles are evolving and have become sophisticated and relatively dynamic. That is why solely relying on Sproles & Kendall’s dimensions to assess the young consumers’ purchasing decision styles is inadequate and create gaps in business/behavior strategy development. By and large, the constructs developed here capture variables the variables that mostly underpin and dominate the considerations within the purchasing decision-making styles and behaviors among millennials.

show abstract

Extending the Consumer Style Inventory to Define Consumer Typologies for Secondhand Clothing Consumption in Poland

Cited by 2 publications

References 22 publications

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Consumer Style Inventory (CSI) Revisited: The case of Millennial Decision-making Style in a Developing Market

Contact Info

Product

Resources

About