{"title":"时尚推荐中用户属性和个人资料定义的专家观点综述与综合","authors":"B. Dahunsi, H. Woelfle, N. Gagliardi, L. E. Dunne","doi":"10.1080/17543266.2023.2261228","DOIUrl":null,"url":null,"abstract":"ABSTRACTA key obstacle in personalised fashion recommendations is the challenge of capturing user physical attributes at a large scale, which limits exclusively computational methods (like machine learning) to readily available attributes whose influence on recommendation accuracy is variable. Expert advice is a potential means of identifying influential user attributes. However, individual experts often disagree or offer conflicting advice. Thus, identifying areas where expert advice is or isn't consistent, in the context of user attributes and profiling is critical. Here, we characterise the breadth of expert definitions of user attributes and profiles through an exhaustive assessment of 156 years of advice literature. Expert definitions of body colouring, shape, and personality attributes are extracted and compared. The range of attribute-value relationships and profile definitions in each domain is described, and coherence among authors for each domain is discussed.KEYWORDS: Fashion recommendationclothing recommendationfashion advicefeature engineeringuser profiles Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by University of Minnesota Sustainable Development Goals Initiative.; US National Science Foundation: [grant number #1715200].","PeriodicalId":39443,"journal":{"name":"International Journal of Fashion Design, Technology and Education","volume":"75 1","pages":"0"},"PeriodicalIF":1.9000,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Review and synthesis of expert perspectives on user attribute and profile definitions for fashion recommendation\",\"authors\":\"B. Dahunsi, H. Woelfle, N. Gagliardi, L. E. Dunne\",\"doi\":\"10.1080/17543266.2023.2261228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTA key obstacle in personalised fashion recommendations is the challenge of capturing user physical attributes at a large scale, which limits exclusively computational methods (like machine learning) to readily available attributes whose influence on recommendation accuracy is variable. Expert advice is a potential means of identifying influential user attributes. However, individual experts often disagree or offer conflicting advice. Thus, identifying areas where expert advice is or isn't consistent, in the context of user attributes and profiling is critical. Here, we characterise the breadth of expert definitions of user attributes and profiles through an exhaustive assessment of 156 years of advice literature. Expert definitions of body colouring, shape, and personality attributes are extracted and compared. The range of attribute-value relationships and profile definitions in each domain is described, and coherence among authors for each domain is discussed.KEYWORDS: Fashion recommendationclothing recommendationfashion advicefeature engineeringuser profiles Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by University of Minnesota Sustainable Development Goals Initiative.; US National Science Foundation: [grant number #1715200].\",\"PeriodicalId\":39443,\"journal\":{\"name\":\"International Journal of Fashion Design, Technology and Education\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Fashion Design, Technology and Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/17543266.2023.2261228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fashion Design, Technology and Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17543266.2023.2261228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
Review and synthesis of expert perspectives on user attribute and profile definitions for fashion recommendation
ABSTRACTA key obstacle in personalised fashion recommendations is the challenge of capturing user physical attributes at a large scale, which limits exclusively computational methods (like machine learning) to readily available attributes whose influence on recommendation accuracy is variable. Expert advice is a potential means of identifying influential user attributes. However, individual experts often disagree or offer conflicting advice. Thus, identifying areas where expert advice is or isn't consistent, in the context of user attributes and profiling is critical. Here, we characterise the breadth of expert definitions of user attributes and profiles through an exhaustive assessment of 156 years of advice literature. Expert definitions of body colouring, shape, and personality attributes are extracted and compared. The range of attribute-value relationships and profile definitions in each domain is described, and coherence among authors for each domain is discussed.KEYWORDS: Fashion recommendationclothing recommendationfashion advicefeature engineeringuser profiles Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by University of Minnesota Sustainable Development Goals Initiative.; US National Science Foundation: [grant number #1715200].