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Article
Publication date: 13 March 2019

Congying Guan, Shengfeng Qin and Yang Long

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion…

Abstract

Purpose

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.

Design/methodology/approach

This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.

Findings

The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.

Originality/value

The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 7 November 2016

Congying Guan, Shengfeng Qin, Wessie Ling and Guofu Ding

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive…

Abstract

Purpose

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

Design/methodology/approach

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

Findings

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Originality/value

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

Details

International Journal of Clothing Science and Technology, vol. 28 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 18 January 2011

Yuanyuan Yin, Shengfeng Qin and Ray Holland

The purpose of this paper is to investigate how to measure collaborative design performance and, in turn, improve the final design output during a design process, with a…

Abstract

Purpose

The purpose of this paper is to investigate how to measure collaborative design performance and, in turn, improve the final design output during a design process, with a clear objective to develop a design performance measurement (DPM) matrix to measure design project team members' design collaboration performances.

Design/methodology/approach

The methodology adopted in this research uses critical literature reviews, in‐depth focus group interviews and a questionnaire survey.

Findings

The main finding of this study is a DPM matrix that addresses five DPM indicators: efficiency, effectiveness, collaboration, management skill, and innovation, and 25 detailed DPM criteria. It was found that decision‐making efficiency is the most important DPM criterion for collaborative design efficiency; plus delivering to the brief for effectiveness; clear team goal/objectives for collaboration; decision‐making ability for management skill; and competitive advantage for innovation.

Research limitations/implications

As the present study was focused on exploring DPM during a design process, some key DPM criteria which are not measurable during a design development process were not included in this study. The proposed multi‐feedback approach for DPM matrix implementation needs to be validated in future research.

Practical implications

The DPM matrix can be applied to support a design manager in measuring and improving collaborative design performance during a design process, by reviewing and modifying collaborative design development, identifying the design team strengths and weaknesses, improving team communication, and suggesting suitable responsive actions.

Originality/value

The major contribution of this study is the investigation and development of a DPM matrix to measure collaborative design performance during a design process.

Details

International Journal of Productivity and Performance Management, vol. 60 no. 2
Type: Research Article
ISSN: 1741-0401

Keywords

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Article
Publication date: 20 April 2012

Hur‐Li Lee

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Abstract

Purpose

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Design/methodology/approach

Originating from a theoretical stance that situates knowledge organization in its social context, the study applies a multifaceted framework pertaining to five categories of textual data: the Seven Epitomes; biographical information about the classificationist Liu Xin; and the relevant intellectual, political, and technological history.

Findings

The study discovers seven principles contributing to the epistemic foundation of the catalogue's classification: the Han imperial library collection imposed as the literary warrant; government functions considered for structuring texts; classicist morality determining the main classificatory structure; knowledge perceived and organized as a unity; objects, rather than subjects, of concern affecting categories at the main class level; correlative thinking connecting all text categories to a supreme knowledge embodied by the Six Classics; and classicist moral values resulting in both vertical and horizontal hierarchies among categories as well as texts.

Research limitations/implications

A major limitation of the study is its focus on the main classes, with limited attention to subclasses. Future research can extend the analysis to examine subclasses of the same scheme. Findings from these studies may lead to a comparison between the epistemic approach in the target classification and the analytic one common in today's bibliographic classification.

Originality/value

The study is the first to examine in depth the epistemic foundation of traditional Chinese bibliographic classification, anchoring the classification in its appropriate social and historical context.

Details

Journal of Documentation, vol. 68 no. 3
Type: Research Article
ISSN: 0022-0418

Keywords

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Case study
Publication date: 1 August 2016

Feng Tianjun, Zhang Chunyi and Quan Lin

Shanghai ANE Logistics Co., Ltd., established on June 1, 2010, is a business of road part-load logistics for goods from 5 to 300 kilograms. Mr. Wang Yongjun and his…

Abstract

Shanghai ANE Logistics Co., Ltd., established on June 1, 2010, is a business of road part-load logistics for goods from 5 to 300 kilograms. Mr. Wang Yongjun and his management team have spent five consecutive years building ANE into the biggest part-load franchising network in China, and set up a brand new business model, through integration of traditional transport lines, part-load express network and information technology platform.

Details

Management School, Fudan University, vol. no.
Type: Case Study
ISSN: 2632-7635
Published by: Management School, Fudan University

Keywords

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