摘要
为了解决网上购买服装时尺码不合适的问题,采用阶梯层次分析法为消费者推荐最优的服装号型。基于衣长、肩宽、领围、袖长等9项服装数据和背长、肩宽、颈围、臂长等9项人体体型数据,建立号型推荐的阶梯层次结构模型。该模型能够分析各因素对合体女衬衫尺码选择的影响,快速实现合体女衬衫号型推荐。模型实例验证结果表明,实验样本对推荐结果整体满意,该模型可有效地为消费者购买合体女衬衫提供服务,为智能化服装号型推荐奠定基础。
To solve the problem of inappropriate clothing sizes purchased online,the analytic hierarchy process is used to recommend the best clothing sizes for consumers.This method is based on nine items of clothing data,such as length,shoulder width,collar circumference,and sleeve length,and nine human body shape data such as back length,total shoulder width,neck circumference,and arm length,to establish a hierarchical structure model of size recommendation.The model can analyze the importance of various factors that affect the size of a fit blouse,and quickly realize the size recommendation of a fit blouse.The effectiveness of the recommendation method is verified by examples.And the results show that all experimental samples show overall satisfaction with the recommendation results,which can effectively improve the intelligent service for consumers to purchase fit blouses,and lay the foundation for intelligent clothing size recommendation.
作者
赵莉莉
王赛赛
陈敏之
ZHAO Lili;WANG Saisai;CHEN Minzhi(School of Fashion Design and Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China)
出处
《服装学报》
2021年第5期402-407,共6页
Journal of Clothing Research
基金
浙江省自然科学基金项目(LY17E060007)。
关键词
服装号型
层次分析法
合体女衬衫
网络购物
clothing size
analytic hierarchy process
fit blouse
online shopping
