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)|
|Title of host publication||Lecture Notes in Electrical Engineering|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||16|
|State||Published - 2021|
|Name||Lecture Notes in Electrical Engineering|
Bibliographical noteFunding Information:
Acknowledgements This work was supported by the University of Minnesota and by the US National Science Foundation under grant #1715200.
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.