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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: 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

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Article
Publication date: 30 January 2019

Efendi Nasibov, Murat Demir and Alper Vahaplar

Beside the development of technology and accessibility, ease of use, ability to reach various products and compare many products at the same time make online shopping even…

Abstract

Purpose

Beside the development of technology and accessibility, ease of use, ability to reach various products and compare many products at the same time make online shopping even more popular. Despite the great advantages provided by online shopping for either consumers or retailers, there are certain issues that must be solved to improve online shopping advantages. Finding right size is one of the biggest barriers against apparel online retailing. Since the use of apparels is directly related with fitting, choosing right size is becoming more critical for retailers and consumers. The purpose of this paper is to contribute to the solution of the problem.

Design/methodology/approach

For the study, the specific size measurements of male shirts (collar, shoulder, chest, waist, arm length in cm) from four different sizes (small, medium, large, x-large) and from eight different brands were collected and stored in a database. Totally, weight, height and body measurements (collar, shoulder, chest, waist and arm length in cm) of 80 male candidates, between the ages of 18 and 35, were measured individually. These data were then used for experiments.

Findings

Any product with known measurements can be compared with users’ body measurement based on fuzzy logic rule and the best-fitted size can be selected for users. Similarly, using the proposed web design, users are able to see desired products on users with similar body type.

Originality/value

In this study, a new mathematical method based on fuzzy relations for apparel size finder is proposed. Beside, this method can group users based on body measurements in order to find people with similar size.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 2
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 3 April 2020

Preeti Virdi, Arti D. Kalro and Dinesh Sharma

Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely…

Abstract

Purpose

Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely provide suggestions as “people who bought this also bought this” while, consumers are unaware about the source of these recommendations. By amalgamating CF–RS with consumers' social network information, e-commerce sites can offer recommendation from social networks of consumers. These social network embedded systems are known as social recommender systems (SRS). The extant literature has researched on the algorithms and implementation of these systems; however, SRS have not been understood from consumers' psychological perspective. This study aims to qualitatively explore consumers' motives to accept SRS in e-commerce websites.

Design/methodology/approach

This qualitative study is based on in-depth interviews of frequent online shoppers. SRS are currently not very widespread in the Indian e-commerce space; hence, a vignette was shown to respondents before they responded to the questions. Inductive qualitative content analysis method was used to analyse these interviews.

Findings

Three main themes (social-gratification, self-gratification and information-gratification) emerged from the analysis. Out of these, social-gratification acts as an enabler, while self-gratification along with some elements of information-gratification act as inhibitors towards acceptance of social recommendations. Based on these gratifications, we present a conceptual model on consumer's acceptance of social recommendations.

Originality/value

This study is an initial attempt to qualitatively understand consumers' attitudes and acceptance of social recommendations on e-commerce websites, which in itself is a fairly new phenomenon.

Details

Online Information Review, vol. 44 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

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Book part
Publication date: 17 September 2020

Abstract

Details

Sustainable Entrepreneurship: How Entrepreneurs Create Value from Sustainable Opportunities
Type: Book
ISBN: 978-1-80043-147-8

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Article
Publication date: 26 June 2019

Emmelie Gustafsson, Patrik Jonsson and Jan Holmström

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer…

Abstract

Purpose

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products.

Design/methodology/approach

A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature.

Findings

With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling.

Research limitations/implications

Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts.

Practical implications

The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes.

Originality/value

This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

Details

Supply Chain Management: An International Journal, vol. 24 no. 5
Type: Research Article
ISSN: 1359-8546

Keywords

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Article
Publication date: 10 July 2017

Andreas D. Landmark and Børge Sjøbakk

The purpose of this paper is to explore how tracking of products by the use of radio frequency identification (RFID) technology may describe customer behaviour in real-time.

Abstract

Purpose

The purpose of this paper is to explore how tracking of products by the use of radio frequency identification (RFID) technology may describe customer behaviour in real-time.

Design/methodology/approach

The study was conducted as a field experiment, where a commercially available RFID platform was deployed in the fitting rooms of a fashion retail store.

Findings

The study demonstrates an application of in-store RFID tracking to describe customer behaviour, and some practical challenges of utilising such technology. An example typology of four fitting room traits was constructed based on the data collected.

Practical implications

Different customer types most likely require and respond differently to attention from the personnel operating the fitting room area. By identifying customer behaviour in real-time, it is possible to deliver “best practice” shop stewardship and create a more personalised retail experience.

Originality/value

The study is based on real-life retail settings, rather than anecdotal management observations or economic and demographic indicators. To the best of the authors’ knowledge, few contributions combine RFID and consumer behaviour outside conceptual work or laboratory experiments.

Details

International Journal of Retail & Distribution Management, vol. 45 no. 7/8
Type: Research Article
ISSN: 0959-0552

Keywords

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Article
Publication date: 6 March 2017

Xiaoxi Zhou, Hui’e Liang and Zhiya Dong

Today clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by…

Abstract

Purpose

Today clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by effective personalized recommendation. The purpose of this paper is to propose a personalized recommendation model and algorithm based on Kansei engineering, traditional filtering algorithm and the knowledge relating to apparel.

Design/methodology/approach

Users’ perceptual image and the design elements of apparel based on Kansei engineering are discussed to build the mapping relation between the design elements and user ratings employing verbal protocol, semantic differential and partial least squares. The implicit knowledge and emotional needs pertaining to users are accessed using analytic hierarchy process. A personalized recommendation model for apparel online shopping is established and the algorithm for the personalized recommendation process is proposed. To present the personalized recommendation model, men’s plaid shirts are taken as the example, and the recommendations of apparel for online shopping were implemented and ranked in the context of differing users’ emotional needs. A comparison between the traditional model and this model is made to verify the effectiveness.

Findings

The recommendation model is capable of analyzing data and information effectively, and providing fast, personalized apparel recommendation services in accordance with users’ emotional needs. The experimental results suggest that the model is effective.

Originality/value

Similar researches of recommendation mainly focus on the field of computer science, the basic idea of which is using users’ history accessing records or the preferences of other similar users for determination of users’ preferences. Since the attributes of apparel products are not factored in the approach referred above, the issue of personalized recommendation cannot be solved in a really effective way. Combining Kansei engineering and recommendation algorithm, a framework for apparel product recommendation is presented and it is a new way for improvement of recommendations for apparel products on shopping sites.

Details

International Journal of Clothing Science and Technology, vol. 29 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 11 November 2013

Jamal Shahrabi, Esmaeil Hadavandi and Maryam Salehi Esfandarani

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated…

Abstract

Purpose

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues.

Design/methodology/approach

Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set.

Findings

Experimental results show that the proposed model has a good accuracy and can be used as a size recommendation expert system to specify the right size for the customers. By using the proposed model and designing an interface for it, a decision support system was developed as a size recommendation expert system that was used by an apparel sales store. The results were time saving and more satisfying for the customers by selecting the appropriate apparel size for them.

Originality/value

In this paper, as a first study in literature, a hybrid intelligent model for developing a size recommendation expert system based on data clustering and a PNN to enable the salesperson to help the consumer in choosing the right size is proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference to develop a new intelligent classification system by using a PNN. In the last stage, the accuracy of the proposed model is evaluated by using testing data. The proposed model achieved an 87.2 percent accuracy rate that is very promising.

Details

International Journal of Clothing Science and Technology, vol. 25 no. 5
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 1 March 1998

F. Frank Chen

The purpose of this paper is to perform a contrast study on the technologies and contributions of flexible manufacturing systems (FMS) being in use in apparel and metal…

Abstract

The purpose of this paper is to perform a contrast study on the technologies and contributions of flexible manufacturing systems (FMS) being in use in apparel and metal cutting/removal industries. Some significant differences in the technologies and operational characteristics of existing FMS in the soft‐goods apparel industry and in the hard‐goods metal working industry are identified. Detailed discussions on interesting comparable contributions of FMS at various dimensions such as quality, productivity, flexibility, etc., are also presented. Recommendations for future research are also provided.

Details

International Journal of Clothing Science and Technology, vol. 10 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

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