Abstract
Fashion is a domain that poses new and interesting challenges for recommender systems. While most recommendation problems seek a single-point solution (e.g. a product the user will purchase), individual garments must function within a wardrobe system, and must ultimately be matched with other garments to build an outfit. The outfit-building challenge is poorly understood in academic literature and professional practice. Here, we present data from two sources: subjective self-reports from consumers about their outfit-building practices, and assessments (by expert and crowd-sourced assessors) of computer-generated outfit combinations pulled from a real-world wardrobe. Results illuminate the objectives and obstacles of consumers in the daily dressing decision, and support the complexity of building combinations from a large set of individual garments.
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This work was supported by the University of Minnesota and by the US National Science Foundation under grant #1715200.
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Zhang, J., Terveen, L., Dunne, L.E. (2021). The Ensemble-Building Challenge for Fashion Recommendation: Investigation of In-Home Practices and Assessment of Garment Combinations. In: Dokoohaki, N., Jaradat, S., Corona PampÃn, H.J., Shirvany, R. (eds) Recommender Systems in Fashion and Retail. Lecture Notes in Electrical Engineering, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-030-66103-8_6
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