- 智能服装推荐系统(英文版)
- - 作者：张俊杰//袁桦//张赟|责编:苗苗
  - 出版社：中国纺织
  - ISBN：9787518098415
  - 出版日期：2022/11/01
  - 页数：144
- 售价：31.2

内容大纲
本书利用模糊技术建立了智能服装推荐系统，使消费者在电商平台选择服装时，通过输入体型数据、风格关键词和偏好图像等信息，生成虚拟人体模型。本书通过服装专家知识建立了知识库，以便为消费者推荐服装，消费者不仅可以实时看到虚拟试穿效果，而且如果对推荐结果不满意，可将不满意部位的信息反馈给系统，系统利用反馈算法智能学习消费者反馈信息，自适应地更新知识库。推荐模块能根据更新后的知识库进行二次推荐，直到消费者满意为止，成功推荐的结果会存入成功知识库作为今后推荐的依据。
本书可作为服装企业、电商平台研发推荐系统时的指导用书，也可作为服装行业从业者的参考用书。
作者介绍
张俊杰，武汉纺织大学计算机与人工智能学院特别教授，博士，硕士研究生导师。2005年12月获华中科技大学软件工程硕士学位，2017年5月获法国里尔第一大学自动化博士学位。主要研究方向为服装数字化、人工智能与人文社科交叉学科。已发表SCI论文10余篇，主持多项教育部协同创新项目、湖北省教育厅科研项目、中国纺织工业联合会科研项目，获批国家发明专利4项，获批软件著作权10余项；获全国多媒体课件大赛一等奖1项，湖北省教学成果奖一等奖、三等奖各1项，中国纺织工业联合会高等教育教学成果奖多项，获武汉市社会科学优秀成果奖二等奖、陕西高等学校科学技术研究优秀成果奖二等奖和欧洲创意与创新展览会科技奖金奖各1项；指导学生获湖北省“互联网＋”大赛金奖1项。
目录
General Introduction
Chapter 1  E-commerce for Fashion Products
  1.1  E-commerce and Recommendation Systems
  1.2  Fashion Products and Fashion Markets
    1.2.1  The marketing of fashion
    1.2.2  Targeting a market
    1.2.3  Customer profiles
    1.2.4  Seasonal and occasion markets
    1.2.5  Fashion products
  1.3  Fashion Consumer Behavior
    1.3.1  Cultural factor
    1.3.2  Social factor
    1.3.3  Individual factor
    1.3.4  Psychological factor
  1.4  Main Ideas
    1.4.1  Successful Cases Database Module
    1.4.2  Market Forecasting Module
    1.4.3  Knowledge-based Recommendation Module
    1.4.4  Knowledge Updating Module
  1.5  Originality
Chapter 2  Fashion Data Acquisition from Social Networks and Design of Sensory Experiments
  2.1  Social Networks
  2.2  Sensory Evaluation
    2.2.1  Definition and industrial application
    2.2.2  Basic notions about sensory evaluation
  2.3  Design of Experiments
    2.3.1  Preliminary knowledge
    2.3.2  Experiments
  2.4  Conclusions
Chapter 3  Concerned Computational Tools
  3.1  Knowledge and Its Features
  3.2  Fuzzy Sets and Fuzzy Operations
    3.2.1  Fuzzy sets theory
    3.2.2  Standard operations of fuzzy sets
    3.2.3  Properties of fuzzy sets
    3.2.4  Operations on fuzzy relations
    3.2.5  Triangular fuzzy number
    3.2.6  Similarity degree of fuzzy sets
  3.3  FCEM and FuzzyAHP
    3.3.1  FCEM
    3.3.2  Fuzzy AHP
  3.4  Complex Networks
    3.4.1  Scale-free networks
    3.4.2  Small-world networks
  3.5  Markov Chain
  3.6  Case-based Reasoning
  3.7  Organization of the Computational Techniques
Chapter 4  Successful Cases Database Module and Market Forecasting Module
  4.1  Mathematical Formalization
  4.2  Consumer Profile

    4.2.1  Size rules definition
    4.2.2  Fuzzy description of height
    4.2.3  Fuzzy description of "fat/thin
    4.2.4  Application of the FAHP algorithm
    4.2.5  Fuzzy comprehensive evaluation method (FCEM)
    4.2.6  A real case of consumer profile
  4.3  Successful Cases Database Module
    4.3.1  General principle
    4.3.2  Formalization, similarity and reasoning
    4.3.3  A real case of successful cases database module
  4.4  Market Forecasting Module
    4.4.1  General principle
    4.4.2  Market data analysis with Markov Chain
    4.4.3  Complex network
    4.4.4  A real case of market forecasting module
  4.5  Conclusions
Chapter 5  Knowledge-based Recommendation Module and Knowledge Updating Module
  5.1  Knowledge-based Recommendation Module
    5.1.1  General principle
    5.1.2  Data aggregation from various trainees
    5.1.3  Generation of the knowledge base (KB)
    5.1.4  Recommendation process through a real case
  5.2  Knowledge Updating Module
    5.2.1  General principle
    5.2.2  Virtual try-on and evaluation
    5.2.3  Knowledge base updating 1
    5.2.4  Knowledge base updating 2
  5.3  Validation of the Recommendation Modules
  5.4  Conclusions
Chapter 6  General Conclusion and Perspectives
  6.1  General Conclusion
  6.2  Perspectives
References
Appendix Questionnaires for Sensory Evaluations Experiments and Historical Shopping Data

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