Abstract
Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user’s fashion taste. However, none of these consider a user’s preference of price range for individual clothing types or an overall shopping budget for a set of items. In this article, we propose a box recommendation framework—BOXREC—which at first collects user preferences across different item types (namely, top-wear, bottom-wear, and foot-wear) including price range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price ranges), creates all possible combinations of three preferred items (belonging to distinct item types), and verifies each combination using an outfit scoring framework—BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. We create an extensively annotated dataset of male fashion items across various types and categories (each having associated price) and a manually annotated positive and negative formal as well as casual outfit dataset. We consider a set of recently published pairwise compatibility prediction methods as competitors of BOXREC-OSF. Empirical results show superior performance of BOXREC-OSF over the baseline methods. We found encouraging results by performing both quantitative and qualitative analysis of the recommendations produced by BOXREC. Finally, based on user feedback corresponding to the recommendations given by BOXREC, we show that disliked or unpopular items can be a part of attractive outfits.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 2015 International Conference on Learning Representations (ICLR’15). 1--15.Google Scholar
- Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the devil in the details: Delving deep into convolution nets. In Proceedings of the 2014 British Machine Vision Conference (BMVC’14). 1--12.Google ScholarCross Ref
- Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, and Binqiang Zhao. 2019. POG: Personalized outfit generation for fashion recommendation at alibaba ifashion. In Proceedings of the 2019 ACM Conference on Knowledge Discovery 8 Data Mining (KDD’19). 2662--2670.Google ScholarDigital Library
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2016. A unified point-of-interest recommendation framework in location-based social networks. ACM Trans. Intell. Syst. Technol. 8, 1 (2016), 10:1--10:21.Google ScholarDigital Library
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). 248--255.Google ScholarCross Ref
- Xue Dong, Xuemeng Song, Fuli Feng, Peiguang Jing, Xin-Shun Xu, and Liqiang Nie. 2019. Personalized capsule wardrobe creation with garment and user modeling. In Proceedings of the 2019 ACM International Conference on Multimedia (MM’19). 302--310.Google ScholarDigital Library
- Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 2010 International Conference on Artificial Intelligence and Statistics (AISTATS’10). 249--256.Google Scholar
- Nikita Golubtsov, Daniel Galper, and Andrey Filchenkov. 2016. Active adaptation of expert-based suggestions in ladieswear recommender system lookbooksclub via reinforcement learning. In Proceedings of the 2016 Biologically Inspired Cognitive Architectures for Young Scientists (BICA’16). 61--69.Google ScholarCross Ref
- Aristide Grange, Imed Kacem, Karine Laurent, and Sébastien Martin. 2015. On the knapsack problem under merging objects’ constraints. In Proceedings of the 2015 International Conference on Computers 8 Industrial Engineering (CIE’15). 1359--1368.Google Scholar
- Aristide Grange, Imed Kacem, and Sébastien Martin. 2018. Algorithms for the bin packing problem with overlapping items. Comput. Industr. Eng. 115 (2018), 331--341.Google ScholarCross Ref
- Xianjing Han, Xuemeng Song, Yiyang Yao, Xin-Shun Xu, and Liqiang Nie. 2020. Neural compatibility modeling with probabilistic knowledge distillation. IEEE Trans. Image Process. 29 (2020), 871--882.Google ScholarDigital Library
- Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S. Davis. 2017. Learning fashion compatibility with bidirectional LSTMs. In Proceedings of the 2017 ACM International Conference on Multimedia (MM’17). 1078--1086.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 770--778.Google ScholarCross Ref
- Tong He and Yang Hu. 2018. FashionNet: Personalized outfit recommendation with deep neural network. CoRR abs/1810.02443v1 (2018), 1--9.Google Scholar
- Yuhang He and Long Chen. 2016. Fast fashion guided clothing image retrieval: Delving deeper into what feature makes fashion. In Proceedings of the 2016 Asian Conference on Computer Vision (ACCV’16). 134--149.Google Scholar
- Shintami Chusnul Hidayati, Cheng-Chun Hsu, Yu-Ting Chang, Kai-Lung Hua, Jianlong Fu, and Wen-Huang Cheng. 2018. What dress fits me best? fashion recommendation on the clothing style for personal body shape. In Proceedings of the 2018 ACM International Conference on Multimedia (MM’18). 438--446.Google Scholar
- Wei Lin Hsiao and Kristen Grauman. 2018. Creating capsule wardrobes from fashion images. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 1--10.Google ScholarCross Ref
- Wei-Lin Hsiao and Kristen Grauman. 2019. ViBE: Dressing for diverse body shapes. CoRR abs/1912.06697v2 (2019), 1--23.Google Scholar
- M. Hadi Kiapour, Xufeng Han, Svetlana Lazebnik, Alexander C. Berg, and Tamara L. Berg. 2015. Where to buy it:Matching street clothing photos in online shops. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV’15). 3343--3351.Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Comput. Soc. Press 42, 8 (2009), 30--37.Google ScholarDigital Library
- Dongjoo Lee, Sung Eun Park, Minsuk Kahng, Sangkeun Lee, and Sang goo Lee. 2010. Exploiting contextual information from event logs for personalized recommendation. In Computer and Information Science. Studies in Computational Intelligence, R. Lee (Ed.), Vol. 317. 121--139.Google Scholar
- Daniel D. Lee and H. Sebastian Seung. 2000. Algorithms for non-negative matrix factorization. In Proceedings of the 2000 International Conference on Neural Information Processing Systems (NIPS’00). 535--541.Google Scholar
- Kedan Li, Chen Liu, Ranjitha Kumar, and David Forsyth. 2019. Using discriminative methods to learn fashion compatibility across datasets. CoRR abs/1906.07273v1 (2019), 1--10.Google Scholar
- Yuncheng Li, Liangliang Cao, Jiang Zhu, and Jiebo Luo. 2017. Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Trans. Multimedia 19, 8 (2017), 1946--1955.Google ScholarDigital Library
- Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2019. Explainable outfit recommendation with joint outfit matching and comment generation. IEEE Trans. Knowl. Data Eng. 32, 8 (2019), 1502--1516.Google ScholarCross Ref
- Nathan N. Liu, Luheng He, and Min Zhao. 2013. Social temporal collaborative ranking for context aware movie recommendation. ACM Trans. Intell. Syst. Technol. 4, 1 (2013), 15:1--15:26.Google ScholarDigital Library
- Si Liu, Zheng Song, Guangcan Liu, Changsheng Xu, Hanqing Lu, and Shuicheng Yan. 2012. Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12). 3330--3337.Google ScholarDigital Library
- Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In Proceedings of the 2019 International Conference on Learning Representations (ICLR’19). 1--18.Google Scholar
- Silvano Martello and Paolo Toth. 1990. Bin-packing problem. Knapsack Problems: Algorithms and Computer Implementations (1990), 221--245.Google Scholar
- Hai Thanh Nguyen, Thomas Almenningen, Martin Havig, Herman Schistad, Anders Kofod-Petersen, Helge Langseth, and Heri Ramampiaro. 2014. Learning to rank for personalised fashion recommender systems via implicit feedback. In Proceedings of the 2014 International Conference on Mining Intelligence and Knowledge Exploration (MIKE’14). 51--61.Google ScholarCross Ref
- Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In Proceedings of the 2013 International Conference on Machine Learning (ICML’13). 1310--1318.Google Scholar
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Proceedings of the 2007 International Conference on Neural Information Processing Systems (NIPS’07). 1257--1264.Google Scholar
- Xuemeng Song, Fuli Feng, Jinhuan Liu, Zekun Li, Liqiang Nie, and Jun Ma. 2017. NeuroStylist: Neural compatibility modeling for clothing matching. In Proceedings of the 25th ACM International Conference on Multimedia (MM’17). 753--761.Google ScholarDigital Library
- Xuemeng Song, Liqiang Nie, Yinglong Wang, and Gary Marchionini. 2019. Compatibility modeling: Data and knowledge applications for clothing matching. In Synthesis Lectures on Information Concepts, Retrieval, and Services (2019).Google ScholarCross Ref
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 2818--2826.Google ScholarCross Ref
- Reuben Tan, Mariya I. Vasileva, Kate Saenko, and Bryan A. Plummer. 2019. Learning similarity conditions without explicit supervision. In Proceedings of the 2019 IEEE International Conference on Computer Vision (ICCV’19). 10373--10382.Google Scholar
- Pongsate Tangseng, Kota Yamaguchi, and Takayuki Okatani. 2017. Recommending outfits from personal closet. In Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW’17). 2275--2279.Google ScholarCross Ref
- Mariya I. Vasileva, Bryan A. Plummer, Krishna Dusad, Shreya Rajpal, Ranjitha Kumar, and David Forsyth. 2018. Learning type-aware embeddings for fashion compatibility. In Proceedings of the 2018 European Conference on Computer Vision (ECCV’18). 405--421.Google ScholarCross Ref
- Min Xie, Laks V. S. Lakshmanan, and Peter T. Wood. 2010. Breaking out of the box of recommendations: From items to packages. In Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys’10). 151--158.Google Scholar
- Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How transferable are features in deep neural networks? In Proceedings of the 2014 International Conference on Neural Information Processing Systems (NIPS’14). 3320--3328.Google Scholar
- Guoshuai Zhao, Hao Fu, Ruihua Song, Tetsuya Sakai, Zhongxia Chen, Xing Xie, and Xueming Qian. 2019. Personalized reason generation for explainable song recommendation. ACM Trans. Intell. Syst. Technol. 10, 4 (2019), 41:1--41:21.Google ScholarDigital Library
- Yu Zheng, Chen Gao, Xiangnan He, Yong Li, and Depeng Jin. 2020. Price-aware recommendation with graph convolutional networks. In Proceedings of the 2020 IEEE International Conference on Data Engineering (ICDE’20). 275--286.Google ScholarCross Ref
Index Terms
- BOXREC: Recommending a Box of Preferred Outfits in Online Shopping
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