Unused and underutilized clothing is a major contributor to the environmental impact of the apparel industry. To reduce this underutilization, we need to implement ways to maximize clothing use. Artificially intelligent decision support may help users make better purchase decisions as well as daily dressing decisions. However, learning relationships between user and garment features is challenging due to the sparsity of data and the lack of validated expert models. One way to bridge this gap and inform clothing recommender system development is to understand how professional stylists choose outfits that maximize clothing use and satisfaction of clients. The purpose of this study was to understand how professional stylists make outfit and garment decisions for clients. This study used a qualitative approach to collect data from 12 professional stylists with varying areas of specialization on their decision-making process. Data were collected through semi-structured interviews and analyzed using thematic analysis. Findings show client features, garment features and the consultation process as the main factors in decision making. Consequently these factors could be integrated in design of recommender systems that increase consumers’ clothing utilization.
|Original language||English (US)|
|Title of host publication||Lecture Notes in Electrical Engineering|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||22|
|State||Published - 2021|
|Name||Lecture Notes in Electrical Engineering|
Bibliographical noteFunding Information:
Acknowledgements This work was supported by the US National Science Foundation under grant#1715200.
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Apparel recommenders
- Information retrieval
- Information systems
- Qualitative study
- Recommender systems
- Requirements elicitation
- Retrieval tasks and goals