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E. Simo-Serra, H. Ishikawa (2016)
Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
K. Simonyan, Andrew Zisserman (2014)
Very Deep Convolutional Networks for Large-Scale Image RecognitionCoRR, abs/1409.1556
A. Hagberg, D. Schult, P. Swart, JM Hagberg (2008)
Exploring Network Structure, Dynamics, and Function using NetworkX
Christopher Thomas, Adriana Kovashka (2015)
Seeing Behind the Camera: Identifying the Authorship of a Photograph2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
M. Bastian, Sebastien Heymann, Mathieu Jacomy (2009)
Gephi: An Open Source Software for Exploring and Manipulating NetworksProceedings of the International AAAI Conference on Web and Social Media
R. Schifanella, Miriam Redi, L. Aiello (2015)
An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
Tianqiang Liu, Aaron Hertzmann, Wilmot Li, T. Funkhouser (2015)
Style compatibility for 3D furniture modelsACM Transactions on Graphics (TOG), 34
Ilkay Yildiz, E. Cansizoglu, Hantian Liu, Peter Golbus, Ozan Tezcan, Jae-Woo Choi (2020)
Deep Ranking for Style-Aware Room Recommendations (Student Abstract)
Wei-Lin Hsiao, K. Grauman (2017)
Learning the Latent “Look”: Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images2017 IEEE International Conference on Computer Vision (ICCV)
Alex Burnap, J. Hartley, Yanxin Pan, Rich Gonzalez, P. Papalambros (2015)
Balancing design freedom and brand recognition in the evolution of automotive brand stylingDesign Science, 2
R. Bradley, M. Terry (1952)
RANK ANALYSIS OF INCOMPLETE BLOCK DESIGNSBiometrika
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015)
Deep LearningNature, 521
R. Bradley, M. Terry (1952)
RANK ANALYSIS OF INCOMPLETE BLOCK DESIGNS THE METHOD OF PAIRED COMPARISONSBiometrika, 39
Tse-Yu Pan, Yi-Zhu Dai, Min-Chun Hu, Wen-Huang Cheng (2019)
Furniture style compatibility recommendation with cross-class triplet lossMultimedia Tools and Applications, 78
Sehoon Ha, C. Liu (2014)
Iterative Training of Dynamic Skills Inspired by Human Coaching TechniquesACM Transactions on Graphics (TOG), 34
Sagnik Dhar, Vicente Ordonez, Tamara Berg (2011)
High level describable attributes for predicting aesthetics and interestingnessCVPR 2011
Sean Bell, K. Bala (2015)
Learning visual similarity for product design with convolutional neural networksACM Transactions on Graphics (TOG), 34
E. Simo-Serra, S. Fidler, F. Moreno-Noguer, R. Urtasun (2015)
Neuroaesthetics in fashion: Modeling the perception of fashionability2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
I. Lim, Anne Gehre, L. Kobbelt (2016)
Identifying Style of 3D Shapes using Deep Metric LearningComputer Graphics Forum, 35
R. Bradley, M. Terry (1952)
Rank Analysis of Incomplete Block Designs: I. The Method of Paired ComparisonsBiometrika, 39
Sandhya Sachidanandan, Richard Luong, Emil Joergensen (2019)
Designer-driven add-to-cart recommendationsProceedings of the 13th ACM Conference on Recommender Systems
Elad Hoffer, N. Ailon (2014)
Deep Metric Learning Using Triplet Network
E. Cansizoglu, Hantian Liu, Tomer Weiss, Archi Mitra, Dhaval Dholakia, Jae-Woo Choi, D. Wulin (2019)
Room Style Estimation for Style-Aware Recommendation2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
J. Bromley, James Bentz, L. Bottou, Isabelle Guyon, Yann LeCun, C. Moore, Eduard Säckinger, Roopak Shah (1993)
Signature Verification Using A "Siamese" Time Delay Neural NetworkInt. J. Pattern Recognit. Artif. Intell., 7
Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai, Jiajing Xu, Jeff Donahue, Sarah Tavel (2015)
Visual Search at PinterestProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Z. Lun, E. Kalogerakis, A. Sheffer (2015)
Elements of styleACM Transactions on Graphics (TOG), 34
Tomer Weiss, Alan Litteneker, N. Duncan, Masaki Nakada, Chenfanfu Jiang, L. Yu, Demetri Terzopoulos (2018)
Fast and Scalable Position-Based Layout SynthesisIEEE Transactions on Visualization and Computer Graphics, 25
Xiaoling Gu, Yongkang Wong, Pai Peng, L. Shou, Gang Chen, M. Kankanhalli (2017)
Understanding Fashion Trends from Street Photos via Neighbor-Constrained Embedding LearningProceedings of the 25th ACM international conference on Multimedia
Style Similarity as Feedback
Ruizhen Hu, Wenchao Li, O. Kaick, Hui Huang, Melinos Averkiou, D. Cohen-Or, Hao Zhang (2017)
Co-locating style-defining elements on 3D shapesACM Trans. Graph., 36
Kyung Hyun, Ji-Hyun Lee, Minki Kim (2017)
The gap between design intent and user response: identifying typical and novel car design elements among car brands for evaluating visual significanceJournal of Intelligent Manufacturing, 28
Sergey Karayev, Matthew Trentacoste, Helen Han, A. Agarwala, Trevor Darrell, Aaron Hertzmann, Holger Winnemoeller (2013)
Recognizing Image StyleArXiv, abs/1311.3715
[Matching and recommending products is beneficial for both customers and companies. With the rapid increase in home goods e-commerce, there is an increasing demand for quantitative methods for providing such recommendations for millions of products. This approach is facilitated largely by online stores such as Amazon and Wayfair, in which the goal is to maximize overall sales. Instead of focusing on overall sales, we take a product design perspective, by employing big-data analysis for determining the design qualities of a highly recommended product. Specifically, we focus on the visual style compatibility of such products. We build off previous work which implemented a style-based similarity metric for thousands of furniture products. Using analysis and visualization, we extract attributes of furniture products that are highly compatible style-wise. We propose a designer in-the-loop workflow that mirrors methods of displaying similar products to consumers browsing e-commerce websites. Our findings are useful when designing new products, since they provide insight regarding what furniture will be strongly compatible across multiple styles, and hence, more likely to be recommended.]
Published: Sep 24, 2020
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