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Apparel recommendation system evolution: an empirical review

Congying Guan (Department of Design, Northumbria University, Newcastle upon Tyne, UK)
Shengfeng Qin (Department of Design, Northumbria University, Newcastle upon Tyne, UK)
Wessie Ling (Department of Design, Northumbria University, Newcastle upon Tyne, UK)
Guofu Ding (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 7 November 2016




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.


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.


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.


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.



Guan, C., Qin, S., Ling, W. and Ding, G. (2016), "Apparel recommendation system evolution: an empirical review", International Journal of Clothing Science and Technology, Vol. 28 No. 6, pp. 854-879.



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Copyright © 2016, Emerald Group Publishing Limited

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