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Abstract

 

Recent years have witnessed a growing trend of building the capsule wardrobe by minimizing and diversifying the garments in their messy wardrobes. Thanks to the recent advances in multimedia techniques, many researches have promoted the automatic creation of capsule wardrobes by the garment modeling. Nevertheless, most capsule wardrobes generated by existing methods fail to consider the user profile, including the user preferences, body shapes and consumption habits, which indeed largely affects the wardrobe creation. To this end, we introduce a combinatorial optimizationbased personalized capsule wardrobe creation framework, named PCW-DC, which jointly integrates both garment modeling (i.e., wardrobe compatibility) and user modeling (i.e., preferences, body shapes). To justify our model, we construct a dataset, named bodyFashion, which consists of 116,532 user-item purchase records on Amazon involving 11,784 users and 75,695 fashion items. Extensive experiments on bodyFashion have demonstrated the effectiveness of our proposed model. As a byproduct, we have released the codes and the data to facilitate the research community.  

Framework

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Figure 1:  Schematic illustration of the scoring model, consisting of the user modeling and garment modeling.

 bodyFashion Dataset

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Figure 2: An example of bodyFashion dataset, which  comprises 116,532 user-item purchase records on Amazon involving 11,784 users and 75,695 fashion items.

Resources

Results of the Personalized Capsule Wardrobe Creation

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Figure 3:  An example of PCW creation. First line: Results of PCW created from user original wardrobe by the proposed PCWDC method and its variants. Second line: Possible outfits provided by the created personalized capsule wardrobe.

Results of Complementary Item Retrieval

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Figure 4:  Suitable and unsuitable garments for different body shapes derived by our user body shape modeling.

Copyright (C) <2019>  Shandong University

This program is licensed under the GNU General Public License 3.0 (https://www.gnu.org/licenses/gpl-3.0.html). Any derivative work obtained under this license must be licensed under the GNU General Public License as published by the Free Software Foundation, either Version 3 of the License, or (at your option) any later version, if this derivative work is distributed to a third party.

The copyright for the program is owned by Shandong University. For commercial projects that require the ability to distribute the code of this program as part of a program that cannot be distributed under the GNU General Public License, please contact <dongxue.sdu@gmail.com> to purchase a commercial license.

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