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The aim of this paper is to assess the use of social media by Gen Z consumers and the ways they impact on and re-shape their fashion consumption journey. This generational approach uses the lens of uses and gratifications theory (UGT) to explore the customer fashion retail journey from the perspective of the Gen Z consumer.

The research uses an exploratory approach in response to the relative lack of research in to GenZ consumers combined with a need to understand shopping journeys. Mixed methods were used with a first phase of interviews followed by a survey of 102 Gen Z students recruited online in the UK during the COVID-19 pandemic.

The study found that GenZ users of social media for shopping sought gratification from experiences derived from social relationships, entertainment and information. The need for immediate gratification was found in new information and meeting new people to maintain social relationships, learn about products and inform the shopping journey. Further, the research supported the importance of visual images in the affective gratification of shopping needs. Resale sites on social media were favoured for their low prices, information about previously owned fashion items and the opportunity to exercise sustainable fashion choices.

The research advances understanding of fashion shopping journeys through social media and online resale sites. It demonstrates that younger consumers, GenZ, shop through the gratification of experiences informed by their social networks and wider contacts. The linear stages of pre to post–purchase shopping are merged and looped as they exchange information about their shopping journey, from information gathering to post–purchase comments. The role of the brand to these knowledgeable consumers conducting their own resale trade is to facilitate access to and information about their products.

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.

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.

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.

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.

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.