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 optimization-based personalized capsule wardrobe creation framework, named PCW-DC, which jointly integrates both garment modeling (\textiti.e., wardrobe compatibility) and user modeling (\textiti.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.
- Wei-Lin Hsiao and Kristen Grauman. Creating capsule wardrobes from fashion images. In Conference on Computer Vision and Pattern Recognition, pages 7161--7170, 2018.Google ScholarCross Ref
- Ranjitha Kumar and Kristen Vaccaro. An experimentation engine for data-driven fashion systems. In AAAI Spring Symposium Series, 2017.Google Scholar
- Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S. Davis. Learning fashion compatibility with bidirectional lstms. In ACM Multimedia Conference on Multimedia Conference, pages 1078--1086, 2017.Google ScholarDigital Library
- Shintami Chusnul Hidayati, Cheng-Chun Hsu, Yu-Ting Chang, Kai-Lung Hua, Jianlong Fu, and Wen-Huang Cheng. What dress fits me best?: Fashion recommendation on the clothing style for personal body shape. In ACM Multimedia Conference on Multimedia Conference, pages 438--446, 2018.Google ScholarDigital Library
- Hosnieh Sattar, Gerard Pons-Moll, and Mario Fritz. Fashion is taking shape: Understanding clothing preference based on body shape from online sources. In IEEE Winter Conference on Applications of Computer Vision, pages 968--977, 2019.Google Scholar
- Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley. Visually-aware fashion recommendation and design with generative image models. In IEEE International Conference on Data Mining, pages 207--216, 2017.Google ScholarCross Ref
- Ruining He and Julian McAuley. VBPR: visual bayesian personalized ranking from implicit feedback. In AAAI Conference on Artificial Intelligence, pages 144--150, 2016.Google Scholar
- Ruining He, Chunbin Lin, Jianguo Wang, and Julian McAuley. Sherlock: Sparse hierarchical embeddings for visually-aware one-class collaborative filtering. In International Joint Conference on Artificial Intelligence, pages 3740--3746, 2016.Google Scholar
- Liqiang Nie, Xuemeng Song, and Tat-Seng Chua. Learning from Multiple Social Networks. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers, 2016.Google Scholar
- Markus Brill, Edith Elkind, Ulle Endriss, and Umberto Grandi. Pairwise diffusion of preference rankings in social networks. In International Joint Conference on Artificial Intelligence, pages 130--136, 2016.Google Scholar
- Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In International Conference on Data Mining, pages 263--272, 2008.Google ScholarDigital Library
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, and Qiang Yang. One-class collaborative filtering. In International Conference on Data Mining, pages 502--511, 2008.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: bayesian personalized ranking from implicit feedback. In Conference on Uncertainty in Artificial Intelligence, pages 452--461, 2009.Google Scholar
- Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, and Zheng Qin. Aesthetic-based clothing recommendation. In Proceedings of World Wide Web Conference on World Wide Web, pages 649--658, 2018.Google ScholarDigital Library
- Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. Image-based recommendations on styles and substitutes. In ACM SIGIR Conference on Research and Development in Information Retrieval, pages 43--52, 2015.Google Scholar
- Andreas Veit, Balazs Kovacs, Sean Bell, Julian McAuley, Kavita Bala, and Serge J. Belongie. Learning visual clothing style with heterogeneous dyadic co-occurrences. In IEEE International Conference on Computer Vision, pages 4642--4650, 2015.Google ScholarDigital Library
- Xuemeng Song, Fuli Feng, Jinhuan Liu, Zekun Li, Liqiang Nie, and Jun Ma. Neurostylist: Neural compatibility modeling for clothing matching. In ACM Multimedia Conference on Multimedia Conference, pages 753--761, 2017.Google Scholar
- Yuncheng Li, Liangliang Cao, Jiang Zhu, and Jiebo Luo. Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Trans. Multimedia, 19(8):1946--1955, 2017.Google ScholarDigital Library
- Long Chen and Yuhang He. Dress fashionably: Learn fashion collocation with deep mixed-category metric learning. In AAAI Conference on Artificial Intelligence, pages 2103--2110, 2018.Google Scholar
- Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, and Tat-Seng Chua. Transnfcm: Translation-based neural fashion compatibility modeling. In AAAI Conference on Artificial Intelligence, 2019.Google ScholarCross Ref
- Mariya I. Vasileva, Bryan A. Plummer, Krishna Dusad, Shreya Rajpal, Ranjitha Kumar, and David A. Forsyth. Learning type-aware embeddings for fashion compatibility. In European Conference on Computer Vision, pages 405--421, 2018.Google ScholarCross Ref
- Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Rijke. Improving outfit recommendation with co-supervision of fashion generation. In International World Wide Web Conference, 2019.Google ScholarDigital Library
- Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Wei Liu, and Liqiang Nie. Neural compatibility modeling with attentive knowledge distillation. In International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 5--14, 2018.Google ScholarDigital Library
- Zhengzhong Zhou, Xiu Di, Wei Zhou, and Liqing Zhang. Fashion sensitive clothing recommendation using hierarchical collocation model. In ACM Multimedia Conference on Multimedia Conference, pages 1119--1127, 2018.Google ScholarDigital Library
- Wenguan Wang, Yuanlu Xu, Jianbing Shen, and Song-Chun Zhu. Attentive fashion grammar network for fashion landmark detection and clothing category classification. In Conference on Computer Vision and Pattern Recognition, pages 4271--4280, 2018.Google ScholarCross Ref
- Jingyuan Liu and Hong Lu. Deep fashion analysis with feature map upsampling and landmark-driven attention. In European Conference on Computer Vision Workshops, pages 30--36, 2018.Google Scholar
- Zunlei Feng, Zhenyun Yu, Yezhou Yang, Yongcheng Jing, Junxiao Jiang, and Mingli Song. Interpretable partitioned embedding for customized multi-item fashion outfit composition. In International Conference on Multimedia Retrieval, pages 143--151, 2018.Google ScholarDigital Library
- Ziaeefard M, Camacaro J, and Bessega C. Hierarchical feature map characterization in fashion interpretation. In Conference on Computer and Robot Vision, pages 88--94, 2018.Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: bayesian personalized ranking from implicit feedback. In Uncertainty in Artificial Intelligence, pages 452--461, 2009.Google Scholar
- Xishan Zhang, Jia Jia, Ke Gao, Yongdong Zhang, Dongming Zhang, Jintao Li, and Qi Tian. Trip outfits advisor: Location-oriented clothing recommendation. IEEE Trans. Multimedia, 19(11):2533--2544, 2017.Google ScholarCross Ref
- Shuhui Jiang, Yue Wu, and Yun Fu. Deep bi-directional cross-triplet embedding for cross-domain clothing retrieval. In ACM Multimedia Conference on Multimedia Conference, pages 52--56, 2016.Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015.Google Scholar
- Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1096--1104, 2016.Google ScholarCross Ref
- Hossein Talebi and Peyman Milanfar. NIMA: neural image assessment. IEEE Trans. Image Processing, 27(8):3998--4011, 2018.Google ScholarCross Ref
- Brendan J Frey and Delbert Dueck. Clustering by passing messages between data points. science, 315(5814):972--976, 2007.Google Scholar
- Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(Nov):2579--2605, 2008.Google Scholar
- Yingying Deng, Fan Tang, Weiming Dong, Hanxing Yao, and Bao-Gang Hu. Style-oriented representative paintings selection. In Special Interest Group on Computer Graphics, pages 12:1--12:2, 2017.Google Scholar
Index Terms
- Personalized Capsule Wardrobe Creation with Garment and User Modeling
Recommendations
Fashion Compatibility Modeling through a Multi-modal Try-on-guided Scheme
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information RetrievalRecent years have witnessed a growing trend of fashion compatibility modeling, which scores the matching degree of the given outfit and then provides people with some dressing advice. Existing methods have primarily solved this problem by analyzing the ...
Personalized fashion outfit generation with user coordination preference learning
AbstractThis paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community ...
Highlights- It works on recommending new, rather than pre-defined, outfits for individual users.
- Novel formulation for personalized outfit generation task with better performance.
- A new TOG framework organically integrates personalization & ...
Interweaving Trend and User Modeling for Personalized News Recommendation
WI-IAT '11: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01In this paper, we study user modeling on Twitter and investigate the interplay between personal interests and public trends. To generate semantically meaningful user profiles, we present a framework that allows us to enrich the semantics of individual ...
Comments