Apparel Recommendation System Evolution: An empirical review

Guan, Congying, Qin, Sheng-feng, Ling, Wessie and Ding, Guofu (2016) Apparel Recommendation System Evolution: An empirical review. International Journal of Clothing Science and Technology, 28 (6). pp. 854-879. ISSN 0955-6222

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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, this paper undertakes an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this article 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. We 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, personalized design systems, and recommendation systems. Our research interest is focused 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 were tested in system modelling, existing research lacks of concerns in terms of apparel and users profiles research. Thus, from the review, we 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.

Item Type: Article
Uncontrolled Keywords: Features extraction, Knowledge learning, Apparel design, Apparel recommendation system, Apparel retailing
Subjects: W200 Design studies
Department: Faculties > Arts, Design and Social Sciences > Design
Depositing User: Becky Skoyles
Date Deposited: 06 Jun 2016 10:26
Last Modified: 01 Aug 2021 12:46

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