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.
Original language | English (US) |
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Title of host publication | Lecture Notes in Electrical Engineering |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 101-116 |
Number of pages | 16 |
DOIs | |
State | Published - 2021 |
Publication series
Name | Lecture Notes in Electrical Engineering |
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Volume | 734 |
ISSN (Print) | 1876-1100 |
ISSN (Electronic) | 1876-1119 |
Bibliographical note
Funding Information:Acknowledgements This work was supported by the University of Minnesota and by the US National Science Foundation under grant #1715200.
Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.