受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

Fashion styles adopted every day are an important aspect of culture, and style trend analysis helps provide a deeper understanding of our societies and cultures. To analyze everyday fashion trends from the humanities perspective, we need a digital archive that includes images of what people wore in their daily lives over an extended period. In fashion research, building digital fashion image archives has attracted significant attention. However, the existing archives are not suitable for retrieving everyday fashion trends. In addition, to interpret how the trends emerge, we need non-fashion data sources relevant to why and how people choose fashion. In this study, we created a new fashion image archive called Chronicle Archive of Tokyo Street Fashion (CAT STREET) based on a review of the limitations in the existing digital fashion archives. CAT STREET includes images showing the clothing people wore in their daily lives during the period 1970–2017, which contain timestamps and street location annotations. We applied machine learning to CAT STREET and found two types of fashion trend patterns. Then, we demonstrated how magazine archives help us interpret how trend patterns emerge. These empirical analyses show our approach’s potential to discover new perspectives to promote an understanding of our societies and cultures through fashion embedded in consumers’ daily lives.

DOI： 10.1007/978-3-030-91669-5_15

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Studies in Classification, Data Analysis, and Knowledge Organization  

Fashion behavior, i.e., consumer behavior with regard to choosing, purchasing, and wearing clothing, is diverse and intricate because it springs from consumers’ complicated contexts. It is important for those in the fashion industry to understand the full picture of fashion behavior, find ways to sustain their customers’ interest in fashion, and promote their products appropriately. However, as far as we know, there has been little comprehensive research on the diverse and intricate chain reactions involved in fashion behavior. In light of this gap in our understanding, the aim of this research is to describe consumer fashion behavior holistically using a Bayesian network approach with actual business data. We train four types of Bayesian network with different constraints, finding that different types of network are best suited to describing the behavior of men and women. Then, we validate the theoretical consistency of the selected networks by comparing their results with the findings of previous studies. In this paper, we mainly focus on fashion-related problems and consumer impulse buying behavior because of space limitations. In addition to these academic contributions, we demonstrate how businesses could apply Bayesian networks in describing fashion behavior, allowing marketers to use sensitivity analysis to devise potential marketing campaigns and change their customers’ behavior.

DOI： 10.1007/978-981-15-3311-2_32

Scopus

記述言語：日本語  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： 10.11295/marketingscience.230105

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：機関テクニカルレポート，技術報告書，プレプリント等  

DOI： https://dx.doi.org/10.2139/ssrn.3419708

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：機関テクニカルレポート，技術報告書，プレプリント等  

DOI： https://doi.org/10.48550/arXiv.2303.01308

記述言語：英語   掲載種別：機関テクニカルレポート，技術報告書，プレプリント等  

記述言語：日本語  

開催年月日： 2024年1月

記述言語：日本語   会議種別：公開講演，セミナー，チュートリアル，講習，講義等  

開催地：名古屋   国名：日本国  

開催年月日： 2023年12月

記述言語：日本語   会議種別：ポスター発表  

開催地：明治大学   国名：日本国  

開催年月日： 2023年12月

記述言語：日本語   会議種別：ポスター発表  

開催地：明治大学   国名：日本国  

開催年月日： 2023年12月

記述言語：日本語   会議種別：ポスター発表  

開催地：明治大学   国名：日本国  

開催年月日： 2021年12月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン開催   国名：日本国  

対象： 社会人・一般

種別：セミナー・ワークショップ