Abstract
The massive size of fashion items catalogues, jointly with the explosive number of product combinations and specific customer preferences and traits—in what is known as the information overload problem—tend to degrade customers’ online experience. To mitigate the effects of this problem and to improve customers’ online experience, Recommender Systems (RSs) have been proposed. This chapter provides an overview of Artificial Intelligence techniques for personalised RSs in online fashion retail stores. An introductory description of RSs is provided, including their components, potential applications, current commercial availability and limitations, and possible evolutionary avenues. Customer Models (CMs) are described and highlighted as a key component that allows RSs to provide personalised recommendations. The chapter also briefly explores how CM/RS combinations may assist stakeholders in fashion industry domains other than sales. Contents are intended as a primer for the seasoned or apprentice fashion professional in academia or industry to realise Fashion CM/RSs’ potential to facilitate fashion online sales or to transition the fashion industry from product-centric to customer-centric operations. Real-world examples illustrate the main concepts; complementary reading sources are provided.
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Notes
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The term “online retail(ing)” is used here to refer to any form of Business to Consumer (B2C) selling fashion products or services over the Internet. It encompasses the modalities of e-, m- (mobile-), s- (social network-), c- (collaborative-), and t- (television-) commerce. “Online shopping” or “e-commerce” may be used interchangeably here for any of these modalities unless otherwise stated.
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Pereira, A.M., de Barros Costa, E., Vieira, T., Landim, A.R.D., Moura, J.A.B. (2023). Helping Online Fashion Customers Help Themselves: Personalised Recommender Systems. In: Bazaki, E., Wanick, V. (eds) Reinventing Fashion Retailing. Palgrave Studies in Practice: Global Fashion Brand Management . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-11185-3_2
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