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Fashion Recommender Systems

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Abstract

The increasing popularity of online fashion and online retail platforms is having a visible impact on the shopping experience of billions of customers, making millions of products available in online catalogs thus eliminating the need for physical visits to various stores and for waiting in long queues or trying on clothes in dressing rooms by providing personalized and affordable deliveries. This in turn has created novel challenges for platform providers, within which proper understanding of fashion choices of shoppers plays a crucial role. Shoppers tend to feel overwhelmed by the sheer choice of the assortment and brands, not being able to receive effective suggestions matching their style preferences as well as not being able to spot the right size and fit during the shopping experience. As a result, recommender systems are gaining momentum by mining through large and diverse silos of product catalogs as well as customer datasets in order to provide personalized recommendations of outfits, complimenting the shopping session with similar and relevant products, understanding and suggesting the correct size and fit for shoppers, recommending with personalized styles and leveraging the social influence affecting the choice of style and buying behavior of new generations of shoppers. To this end, within this chapter we aim to present a state of the art view of the advancements within the field of recommendation systems in the domain of fashion. We discuss in detail the open challenges and provide an outlook on current and future work in this exciting multidisciplinary field.

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References

  1. Zalando. https://en.zalando.de/, 2020. Accessed: 2020

  2. R. Devooght, H. Bersini, Long and short-term recommendations with recurrent neural networks, in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP ’17 (ACM, New York, 2017), pp. 13–21. Code available in: https://github.com/rdevooght/sequence-based-recommendations. Accessed: 2020.

  3. J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, in NIPS 2014 Workshop on Deep Learning, December 2014, 2014

    Google Scholar 

  4. J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, J. Ma, Neural attentive session-based recommendation, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 1419–1428

    Google Scholar 

  5. Q. Liu, R. Mokhosi, Y. Zeng, H. Zhang, STAMP: Short-term attention/memory priority model for session-based recommendation, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018

    Google Scholar 

  6. J.A.S. Rodríguez, J.-C. Wu, M. Khandwawala, Two-stage session-based recommendations with candidate rank embeddings, in Fashion Recommender Systems (Springer, New York, 2020)

    Google Scholar 

  7. F. Aiolli, A preliminary study on a recommender system for the million songs dataset challenge, in IIR, 2013, pp. 73–83

    Google Scholar 

  8. Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, M.S. Lew, Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48 (2016)

    Article  Google Scholar 

  9. Data-Driven Fashion Design. https://multithreaded.stitchfix.com/blog/2016/07/14/data-driven-fashion-design, 2016. Accessed: 2020

  10. S. Mahdizadehaghdam, A. Panahi, H. Krim, Sparse generative adversarial network. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019, pp. 3063–3071

    Google Scholar 

  11. N. Kato, H. Osone, K. Oomori, C.W. Ooi, Y. Ochiai, GANs-based clothes design: pattern maker is all you need to design clothing, in Proceedings of the 10th Augmented Human International Conference, 2019, pp. 1–7.

    Google Scholar 

  12. W.C. Kang, C. Fang, Z. Wang, J. McAuley, Visually-aware fashion recommendation and design with generative image models. 2017 IEEE International Conference on Data Mining (ICDM), 2017 November, pp. 207–216

    Google Scholar 

  13. C. Bracher, S. Heinz, R. Vollgraf, Fashion DNA: merging content and sales data for recommendation and article mapping. KDD 2016 Fashion Workshop, August 14, 2016, San Francisco, CA, 2016.

    Google Scholar 

  14. J. Lasserre, K. Rasch, R. Vollgraf, Studio2Shop: from studio photo shoots to fashion articles, in ICPRAM 2018 - Proc. 7th Int. Conf. Pattern Recognit. Appl. Methods, 2018

    Google Scholar 

  15. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778

    Google Scholar 

  16. D. Goncalves, L. Liu, A. Magalhães, How big can style be? Addressing high dimensionality for recommending with style, in First Workshop on Recommender Systems in Fashion, 2019

    Google Scholar 

  17. Farfetch, an online luxury fashion retail platform. https://www.farfetch.com/, 2020. Accessed: 2020

  18. Y. Jing, D. Liu, D. Kislyuk, A. Zhai, J. Xu, J. Donahue, S. Tavel, Visual search at Pinterest, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015 August, pp. 1889–1898

    Google Scholar 

  19. Pinterest. https://www.pinterest.com, 2020. Pinterest. Accessed: 2020

  20. S. Young, Improving library user experience with A/B testing: principles and process, in Weave: Journal of Library User Experience, 2014

    Google Scholar 

  21. Farfetch, a Chinese multinational technology company specializing in e-commerce, retail, Internet, and technology. https://www.alibaba.com/, 2020. Accessed: 2020

  22. Y. Zhang, P. Pan, Y. Zheng, K. Zhao, Y. Zhang, X. Ren, R. Jin, Visual search at Alibaba, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 993–1001

    Google Scholar 

  23. J. Craik, Fashion: The Key Concepts (Bloomsbury Academic, New Delhi, 2009)

    Google Scholar 

  24. K.T. Hansen, The world in dress: anthropological perspectives on clothing, fashion, and culture. Annu. Rev. Anthropol. 33, 369–392 (2004)

    Article  Google Scholar 

  25. K. Zhao, X. Hu, J. Bu, C. Wang, Deep style match for complementary recommendation. AAAI Workshop, WS-17-01 - WS-17-15, 464–470 (2017)

    Google Scholar 

  26. Y. Zhang, H. Lu, W. Niu, J. Caverlee, in Quality-Aware Neural Complementary Item Recommendation, 2018, pp. 77–85

    Google Scholar 

  27. J.-C. Wu, J.A.S. Rodríguez, H.J.C. Pampín, in Session-Based Complementary Fashion Recommendations, 2019, pp. 2–6

    Google Scholar 

  28. T. Kuhn, S. Bourke, L. Brinkmann, T. Buchwald, C. Digan, H. Hache, S. Jaeger, P. Lehmann, O. Maier, S. Matting, Y. Okulovsky, Supporting stylists by recommending fashion style, (2019)

    Google Scholar 

  29. Outfittery, an online personal shopping service for men. https://www.outfittery.de/, 2020. Accessed: 2020

  30. K.W. Church, Word2vec. Nat. Lang. Eng. 23(1), 155–162 (2017)

    Article  Google Scholar 

  31. J. McAuley, C. Targett, Q. Shi, A. Van Den Hengel, Image-based recommendations on styles and substitutes, in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 43–52. Dataset available in: http://jmcauley.ucsd.edu/data/amazon/links.html. Accessed: 2020

  32. V. Bhardwaj, A. Fairhurst, Fast fashion: response to changes in the fashion industry, in International Review of Retail, Distribution and Consumer Research, 2010

    Google Scholar 

  33. P.G. Campos, F. Díez, I. Cantador, Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User-Adapt. Interact. 24(1–2), 67–119 (2014)

    Article  Google Scholar 

  34. K. Hofmann, A. Schuth, A. Bellogin, M. De Rijke, Effects of position bias on click-based recommender evaluation, in European Conference on Information Retrieval (Springer, New York, 2014), pp. 624–630

    Google Scholar 

  35. J. Sherman, C. Shukla, R. Textor, S. Zhang, A.A. Winecoff, Assessing fashion recommendations: A multifaceted offline evaluation approach, 2019. arXiv:1909.04496v1 [cs.IR]

    Google Scholar 

  36. TrueFit, a personalization platform for footwear and apparel retailers. https://www.truefit.com/, 2020. Accessed: 2020

  37. H.J.C. Pampín, H. Jerbi, M.P. O’Mahony, Evaluating the relative performance of collaborative filtering recommender systems. J. Univ. Comput. Sci. 21(13), 1849–1868 (2015)

    MathSciNet  Google Scholar 

  38. Pinterest’s new skin tone beauty search capability. https://newsroom.pinterest.com/en/skintoneranges2020, 2020. Accessed: 2020

  39. P.R. Berthon, L.F. Pitt, K. Plangger, D. Shapiro, Marketing meets web 2.0, social media, and creative consumers: implications for international marketing strategy. Bus. Horiz. 55(3), 261–271 (2012)

    Google Scholar 

  40. Facebook. https://www.facebook.com/, 2020. Facebook. Accessed: 2020

  41. Instagram. https://www.instagram.com, 2020. Instagram. Accessed: 2020

  42. Polyvore. https://www.polyvore.com, 2020. Polyvore. Accessed: 2020

  43. Chictopia. http://www.chictopia.com/, 2020. Chictopia. Accessed: 2020

  44. LookBook. https://www.lookbook.nu/, 2020. LookBook. Accessed: 2020

  45. I. Mohr, The impact of social media on the fashion industry. J. Appl. Bus. Econ. 15(2), 17–22 (2013)

    Google Scholar 

  46. K. Hammar, S. Jaradat, N. Dokoohaki, M. Matskin, Deep text mining of instagram data without strong supervision, in 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) (IEEE, New York, 2018), pp. 158–165.

    Google Scholar 

  47. S. Bakhshi, D.A. Shamma, E. Gilbert, Faces engage us: photos with faces attract more likes and comments on instagram, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’14, New York, NY, 2014 (Association for Computing Machinery, New York, 2014), pp. 965–974

    Google Scholar 

  48. B. Ferwerda, M. Schedl, M. Tkalcic, Predicting personality traits with instagram pictures, in Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015, EMPIRE ’15, New York, NY, 2015 (Association for Computing Machinery, New York, 2015), pp. 7–10

    Google Scholar 

  49. L. Wang, R. Liu, S. Vosoughi, Salienteye: maximizing engagement while maintaining artistic style on instagram using deep neural networks, in Proceedings of the 2020 International Conference on Multimedia Retrieval, ICMR ’20, New York, NY, 2020 (Association for Computing Machinery, New York, 2020), pp. 331–335

    Google Scholar 

  50. K. Hammar, S. Jaradat, N. Dokoohaki, M. Matskin, Deep text classification of instagram data using word embeddings and weak supervision, in Web Intelligence. Preprint (IOS Press, New York, 2020), pp. 1–15

    Google Scholar 

  51. H. Zheng, K. Wu, J.-H. Park, W. Zhu, J. Luo, Personalized fashion recommendation from personal social media data: An item-to-set metric learning approach (2020). Preprint. arXiv:2005.12439

    Google Scholar 

  52. Y. Hu, X. Yi, L.S. Davis, Collaborative fashion recommendation: A functional tensor factorization approach, in Proceedings of the 23rd ACM International Conference on Multimedia, MM ’15, New York, NY, 2015 (Association for Computing Machinery, New York), pp. 129–138

    Google Scholar 

  53. P. Domingos, M. Richardson, Mining the network value of customers, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’01, New York, NY, 2001 (Association for Computing Machinery, New York, 2001), pp. 57–66

    Google Scholar 

  54. L. del Carmen Contreras Chinchilla, K.A.R. Ferreira, Analysis of the behavior of customers in the social networks using data mining techniques, in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016, pp. 623–625.

    Google Scholar 

  55. D. Jiménez-Castillo, R. Sánchez-Fernández, The role of digital influencers in brand recommendation: Examining their impact on engagement, expected value and purchase intention. Int. J. Inf. Manage. 49, 366–376 (2019)

    Article  Google Scholar 

  56. N. Segev, N. Avigdor, E. Avigdor, Measuring influence on instagram: A network-oblivious approach, in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’18, New York, NY, 2018 (Association for Computing Machinery, New York, 2018), pp. 1009–1012

    Google Scholar 

  57. C.-T. Li, S.-D. Lin, M.-K. Shan, Exploiting endorsement information and social influence for item recommendation, in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’11, New York, 2011 (Association for Computing Machinery, New York, 2011), pp. 1131–1132

    Google Scholar 

  58. C. Li, F. Xiong, Social recommendation with multiple influence from direct user interactions. IEEE Access, 5, 16288–16296 (2017)

    Article  Google Scholar 

  59. Q. Zhang, J. Wu, Q. Zhang, P. Zhang, G. Long, C. Zhang, Dual influence embedded social recommendation. World Wide Web 21(4), 849–874 (2018)

    Article  Google Scholar 

  60. F. Eskandanian, N. Sonboli, B. Mobasher, Power of the few: analyzing the impact of influential users in collaborative recommender systems, in Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’19, New York, NY, 2019 (Association for Computing Machinery, New York, 2019), pp. 225–233

    Google Scholar 

  61. P.P. Analytis, D. Barkoczi, P. Lorenz-Spreen, S. Herzog, The structure of social influence in recommender networks, in Proceedings of the Web Conference 2020, WWW ’20, New York, NY, 2020 (Association for Computing Machinery, New York, 2020), pp. 2655–2661

    Google Scholar 

  62. M. Bertini, A. Ferracani, R. Papucci, A.D. Bimbo, Keeping up with the influencers: Improving user recommendation in instagram using visual content, in Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’20 Adjunct, pp. 29–34, New York, NY, 2020 (Association for Computing Machinery, New York, 2020)

    Google Scholar 

  63. Y. Zhang, J. Caverlee, Instagrammers, fashionistas, and me: Recurrent fashion recommendation with implicit visual influence, in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM ’19, New York, NY, 2019 (Association for Computing Machinery, New York, 2019), pp. 1583–1592

    Google Scholar 

  64. S. Jaradat, N. Dokoohaki, U. Wara, M. Goswami, K. Hammar, M. Matskin, TALs: a framework for text analysis, fine-grained annotation, localisation and semantic segmentation, in 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol. 2 (2019), pp. 201–206

    Google Scholar 

  65. R. He, J. McAuley, Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering, in Proceedings of the 25th International Conference on World Wide Web, WWW ’16, page 507–517, Republic and Canton of Geneva, CHE, 2016. International World Wide Web Conferences Steering Committee.

    Google Scholar 

  66. X. Wang, X. He, L. Nie, T.-S. Chua, Item silk road: recommending items from information domains to social users, in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17, New York, NY, 2017 (Association for Computing Machinery, New York, 2017), pp. 185–194

    Google Scholar 

  67. G. Stalk, Customer returns top $10 billion in 2005: most canadian retailers fail to capitalize on this key customer relationship. Canada NewsWire, 2006

    Google Scholar 

  68. T.-M. Choi, Analytical Modeling Research in Fashion Business (Springer, New York, 2016)

    Book  Google Scholar 

  69. C. John Langley Jr., J.J. Coyle, B.J. Gibson, R.A. Novack, E.J. Bardi, Supply Chain Management: A Logistics Perspective, 8th edn. (South Western College, Kentucky, 2008)

    Google Scholar 

  70. E. Ofek, Z. Katona, M. Sarvary, “Bricks and Clicks”: The impact of product returns on the strategies of multichannel retailers. Market. Sci. 30(1), 42–60 (2011)

    Article  Google Scholar 

  71. C. Barry, Happy returns: how to reduce customer returns-and their costs. Catalog Age, 2000. arXiv:2106.03532v1 [cs.LG]

    Google Scholar 

  72. J. Mostard, R. Teunter, The newsboy problem with resalable returns: A single period model and case study. European Journal of Operational Research, 169(1), 81–96 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  73. G. Pisut, L.J. Connell, Fit preferences of female consumers in the usa. J. Fash. Market. Manage. Int. J. 11(3), 366–379 (2007)

    Google Scholar 

  74. C. Ratcliff, How fashion ecommerce retailers can reduce online returns. Blog text, Econsultancy. Saatavissa, 2014

    Google Scholar 

  75. Y. Ha, L. Stoel, Internet apparel shopping behaviors: the influence of general innovativeness. Int. J. Retail. Distrib. Manage. 32(8), 377–385 (2004)

    Article  Google Scholar 

  76. R. Guigourès, Y.K. Ho, E. Koriagin, A.-S. Sheikh, U. Bergmann, R. Shirvany, A hierarchical Bayesian model for size recommendation in fashion, in Proceedings of the 12th ACM Conference on Recommender Systems (ACM, New York, 2018), pp. 392–396

    Google Scholar 

  77. A.-S. Sheikh, R. Guigourès, E. Koriagin, Y.K. Ho, R. Shirvany, U. Bergmann, A deep learning system for predicting size and fit in fashion e-commerce, in Proceedings of the 13th ACM Conference on Recommender Systems (ACM, New York, 2019)

    Google Scholar 

  78. G. Mohammed Abdulla, S. Borar, Size recommendation system for fashion e-commerce, in KDD Workshop on Machine Learning Meets Fashion, 2017

    Google Scholar 

  79. N Weidner, Vanity sizing, body image, and purchase behavior: A closer look at the effects of inaccurate garment labeling (2010)

    Google Scholar 

  80. R. Velazquez, S.M. Chankov, Environmental impact of last mile deliveries and returns in fashion e-commerce: a cross-case analysis of six retailers, in 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2019, pp. 1099–1103

    Google Scholar 

  81. Y. Yu, H.-S. Kim, Online retailers’ return policy and prefactual thinking: an exploratory study of USA and China e-commerce markets. J. Fash. Market. Manage. 23(4), 504–518 (2019)

    Google Scholar 

  82. M.A. Diggins, C. Chen, J. Chen, A review: customer returns in fashion retailing, in Analytical Modeling Research in Fashion Business (Springer, New York, 2016), pp. 31–48

    Google Scholar 

  83. S. Cullinane, M. Browne, E. Karlsson, Y. Wang, Retail clothing returns: a review of key issues, in Contemporary Operations and Logistics (Springer, New York, 2019), pp. 301–322

    Google Scholar 

  84. G. Walsh, M. Möhring, C. Koot, M. Schaarschmidt, Preventive product returns management systems: a review and a model, in ECIS (2014)

    Google Scholar 

  85. Y. Zhang, O. Juhlin, Using crowd sourcing to solve the fitting problem in online fashion sales. Global Fashion Manage. Conf. 1, 62–66 (2015)

    Article  Google Scholar 

  86. A. Vecchi, F. Peng, M. Al-Sayegh, et al., Looking for the perfect fit? Online fashion retail-opportunities and challenges, in Conference Proceedings: The Business & Management Review, vol. 6 (The Academy of Business & Retail Management, London, 2015), pp. 134–146

    Google Scholar 

  87. S. Charts, https://www.adidas.com.sg/help-topics-size_charts.html, 2020. Accessed: 2020 Jan 13

  88. D. Ujević, L. Szirovicza, I. Karabegović, Anthropometry and the comparison of garment size systems in some european countries. Coll. Antropol. 29(1), 71–78 (2005)

    Google Scholar 

  89. S.-J.H. Shin, C.L. Istook, The importance of understanding the shape of diverse ethnic female consumers for developing jeans sizing systems. Int. J. Consum. Stud. 31(2), 135–143 (2007)

    Article  Google Scholar 

  90. M.-E. Faust, S. Carrier, Designing Apparel for Consumers: The Impact of Body Shape and Size (Woodhead Publishing, Sawston, 2014)

    Google Scholar 

  91. One size fits none. https://time.com/how-to-fix-vanity-sizing, 2020. Accessed: 2020 Jan 28

  92. S. Baier, Analyzing customer feedback for product fit prediction (2019). Preprint. arXiv:1908.10896

    Google Scholar 

  93. N. Karessli, R. Guigourès, R. Shirvany, Sizenet: weakly supervised learning of visual size and fit in fashion images, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on FFSS-USAD, 2019

    Google Scholar 

  94. N. Karessli, R. Guigourès, R. Shirvany, Learning size and fit from fashion images, in Springer’s Special Issue on Fashion Recommender Systems, 2020

    Google Scholar 

  95. A. Nestler, N. Karessli, K. Hajjar, R. Weffer, R. Shirvany, Sizeflags: reducing size and fit related returns in fashion e-commerce, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

    Google Scholar 

  96. V. Sembium, R. Rastogi, A. Saroop, S. Merugu, Recommending product sizes to customers, in Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 243–250 (ACM, New York, 2017)

    Google Scholar 

  97. V. Sembium, R. Rastogi, L. Tekumalla, A. Saroop, Bayesian models for product size recommendations, in Proceedings of the 2018 World Wide Web Conference, WWW ’18, 2018, pp. 679–687

    Google Scholar 

  98. R. Misra, M. Wan, J. McAuley, Decomposing fit semantics for product size recommendation in metric spaces, in Proceedings of the 12th ACM Conference on Recommender Systems, 2018, pp. 422–426. Dataset available in: https://cseweb.ucsd.edu/~jmcauley/datasets.html#clothing_fit. Accessed: 2020

  99. K. Dogani, M. Tomassetti, S. De Cnudde, S. Vargas, B. Chamberlain, Learning embeddings for product size recommendations, in SIGIR eCom, Paris, July 2019

    Google Scholar 

  100. J. Lasserre, A.-S. Sheikh, E. Koriagin, U. Bergmann, R. Vollgraf, R. Shirvany, Meta-learning for size and fit recommendation in fashion, in SIAM International Conference on Data Mining (SDM20), 2020

    Google Scholar 

  101. K. Hajjar, J. Lasserre, A. Zhao, R. Shirvany, Attention gets you the right size and fit in fashion, in 14th ACM Conference on Recommender Systems Workshops, Recsys’20 - fashionXrecsys’20 (ACM, New York, 2020)

    Google Scholar 

  102. Y. Yuan, J.-H. Huh, Cloth Size Coding and Size Recommendation System Applicable for Personal Size Automatic Extraction and Cloth Shopping Mall: MUE/FutureTech 2018 (2019), pp. 725–731

    Google Scholar 

  103. M. Januszkiewicz, C.J. Parker, S.G. Hayes, S. Gill, Online virtual fit is not yet fit for purpose: an analysis of fashion e-commerce interfaces (2017)

    Google Scholar 

  104. L. Lefakis, E. Koriagin, J. Lasserre, R. Shirvany, Personalized size recommendations with human in the loop, in Second ICML Workshop on Human in the Loop Learning (HILL) (2020)

    Google Scholar 

  105. N. Thalmann, B. Kevelham, P. Volino, M. Kasap, E. Lyard, 3d web-based virtual try on of physically simulated clothes. Comput.-Aided Des. Appl. 8, 163–174 (2011)

    Article  Google Scholar 

  106. J. Surville, T. Moncoutie, 3d virtual try-on: The avatar at center stage. 2013

    Google Scholar 

  107. F. Peng, A.-S. Mouhannad, Personalised size recommendation for online fashion, in 6th International conference on mass customization and personalization in Central Europe, 2014, pp. 1–6

    Google Scholar 

  108. F. Bogo, A. Kanazawa, C. Lassner, P.V. Gehler, J. Romero, M.J. Black, Keep it SMPL: automatic estimation of 3d human pose and shape from a single image (2016). CoRR abs/1607.08128

    Google Scholar 

  109. G. Pavlakos, L. Zhu, X. Zhou, K. Daniilidis, Learning to estimate 3d human pose and shape from a single color image (2018). CoRR abs/1805.04092

    Google Scholar 

  110. J.J. Heckman, H. Ichimura, P.E. Todd, Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Rev. Econ. Stud. 64(4), 605–654 (1997)

    Article  MATH  Google Scholar 

  111. X. Song, F. Feng, X. Han, X. Yang, W. Liu, L. Nie, Neural compatibility modeling with attentive knowledge distillation, in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018, pp. 5–14

    Google Scholar 

  112. F. Harada, H. Shimakawa, Outfit recommendation with consideration of user policy and preference on layered combination of garments. Int. J. Adv. Comput. Sci. 2, 49–55 (2012)

    Google Scholar 

  113. H. Tsujita, K. Tsukada, K. Kambara, I. Siio, Complete fashion coordinator: a support system for capturing and selecting daily clothes with social networks, in Proceedings of the International Conference on Advanced Visual Interfaces, 2010, pp. 127–132

    Google Scholar 

  114. E. Shen, H. Lieberman, F. Lam, What am i gonna wear? scenario-oriented recommendation, in Proceedings of the 12th International Conference on Intelligent User Interfaces, 2007, pp. 365–368

    Google Scholar 

  115. S. Liu, J. Feng, Z. Song, T. Zhang, H. Lu, C. Xu, S. Yan, Hi, magic closet, tell me what to wear! in Proceedings of the 20th ACM international conference on Multimedia, 2012, pp. 619–628

    Google Scholar 

  116. P. Tangseng, K. Yamaguchi, T. Okatani, Recommending outfits from personal closet, in Proceedings of the IEEE International Conference on Computer Vision Workshops (2017), pp. 2275–2279

    Google Scholar 

  117. C. Zagel, Product experience wall: a context-adaptive outfit recommender system, in Mensch & Computer, 2014, pp. 367–370

    Google Scholar 

  118. T. He, Y. Hu, Fashionnet: Personalized outfit recommendation with deep neural network (2018). Preprint. arXiv:1810.02443

    Google Scholar 

  119. Y. Li, L. Cao, J. Zhu, J. Luo, Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Trans. Multimedia 19(8), 1946–1955 (2017)

    Article  Google Scholar 

  120. X. Chen, H. Chen, H. Xu, Y. Zhang, Y. Cao, Z. Qin, H. Zha, Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation, in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, pp. 765–774

    Google Scholar 

  121. Y.-G. Shin, Y.-J. Yeo, M.-C. Sagong, S.-W. Ji, S.-J. Ko, Deep fashion recommendation system with style feature decomposition, in 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin) (IEEE, New York, 2019), pp. 301–305

    Google Scholar 

  122. A. Veit, B. Kovacs, S. Bell, J. McAuley, K. Bala, S. Belongie, Learning visual clothing style with heterogeneous dyadic co-occurrences, in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 4642–4650

    Google Scholar 

  123. M.I. Vasileva, B.A. Plummer, K. Dusad, S. Rajpal, R. Kumar, D. Forsyth, Learning type-aware embeddings for fashion compatibility, in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 390–405. Dataset available in: https://github.com/xthan/polyvore. Accessed: 2020

  124. L.F. Polanía, S. Gupte, Learning fashion compatibility across apparel categories for outfit recommendation, in 2019 IEEE International Conference on Image Processing (ICIP) (IEEE, New York, 2019), pp. 4489–4493

    Google Scholar 

  125. E.M. Bettaney, S.R. Hardwick, O. Zisimopoulos, B.P. Chamberlain, Fashion outfit generation for e-commerce (2019). Preprint arXiv:1904.00741

    Google Scholar 

  126. W.-C. Kang, E. Kim, J. Leskovec, C. Rosenberg, J. McAuley, Complete the look: scene-based complementary product recommendation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 10532–10541. Dataset available in: https://github.com/kang205/STL-Dataset. Accessed: 2020

  127. P. Tangseng, T. Okatani, Toward explainable fashion recommendation, in The IEEE Winter Conference on Applications of Computer Vision, 2020, pp. 2153–2162

    Google Scholar 

  128. Z. Feng, Z. Yu, Y. Yang, Y. Jing, J. Jiang, M. Song, Interpretable partitioned embedding for customized multi-item fashion outfit composition, in Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018, pp. 143–151

    Google Scholar 

  129. X. Wang, B. Wu, Y. Zhong, Outfit compatibility prediction and diagnosis with multi-layered comparison network, in Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 329–337

    Google Scholar 

  130. Y. Lin, P. Ren, Z. Chen, Z. Ren, J. Ma, M. de Rijke, et al., Explainable fashion recommendation with joint outfit matching and comment generation (2018). Preprint. arXiv:1806.08977

    Google Scholar 

  131. X. Song, F. Feng, J. Liu, Z. Li, L. Nie, J. Ma, Neurostylist: Neural compatibility modeling for clothing matching, in Proceedings of the 25th ACM International Conference on Multimedia, 2017, pp. 753–761

    Google Scholar 

  132. W.-L. Hsiao, I. Katsman, C.-Y. Wu, D. Parikh, K. Grauman, Fashion++: minimal edits for outfit improvement, in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 5047–5056

    Google Scholar 

  133. E. Simo-Serra, S. Fidler, F. Moreno-Noguer, R. Urtasun, Neuroaesthetics in fashion: modeling the perception of fashionability, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 869–877

    Google Scholar 

  134. D. Banerjee, K.S. Rao, S. Sural, N. Ganguly, Boxrec: recommending a box of preferred outfits in online shopping. ACM Trans. Intell. Syst. Technol. 11(6), 1–28 (2020)

    Article  Google Scholar 

  135. Y. Jiang, X. Qianqian, X. Cao, Outfit recommendation with deep sequence learning. in 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) (IEEE, New York, 2018), pp. 1–5

    Google Scholar 

  136. Y. Jiang, Q. Xu, X. Cao, Q. Huang, Who to ask: An intelligent fashion consultant, in Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018, pp. 525–528

    Google Scholar 

  137. Z. Wang, H. Quan, Fashion outfit composition combining sequential learning and deep aesthetic network, in 2019 International Joint Conference on Neural Networks (IJCNN) (IEEE, New York, 2019), pp. 1–7

    Google Scholar 

  138. X. Han, Z. Wu, Y.-G. Jiang, L.S. Davis, Learning fashion compatibility with bidirectional LSTMs, in Proceedings of the 25th ACM International Conference on Multimedia, 2017, pp. 1078–1086

    Google Scholar 

  139. T. Nakamura, R. Goto, Outfit generation and style extraction via bidirectional LSTM and autoencoder (2018). Preprint. arXiv:1807.03133

    Google Scholar 

  140. A. Graves, N. Jaitly, A.-R. Mohamed, Hybrid speech recognition with deep bidirectional LSTM, in 2013 IEEE workshop on automatic speech recognition and understanding (IEEE, New York, 2013), pp. 273–278

    Book  Google Scholar 

  141. S. Jaradat, N. Dokoohaki, M. Matskin, Outfit2vec: Incorporating clothing hierarchical metadata into outfits’ recommendation, in Fashion Recommender Systems (Springer, New York, 2020), pp. 87–107

    Google Scholar 

  142. Y. Lin, M. Moosaei, H. Yang, Outfitnet: fashion outfit recommendation with attention-based multiple instance learning, in Proceedings of the Web Conference 2020, 2020, pp. 77–87

    Google Scholar 

  143. Y. Hu, X. Yi, L.S. Davis, Collaborative fashion recommendation: a functional tensor factorization approach, in Proceedings of the 23rd ACM International Conference on Multimedia, 2015, pp. 129–138

    Google Scholar 

  144. Y. Lin, P. Ren, Z. Chen, Z. Ren, J. Ma, M. de Rijke, Improving outfit recommendation with co-supervision of fashion generation, in The World Wide Web Conference, 2019, pp. 1095–1105

    Google Scholar 

  145. W. Chen, P. Huang, J. Xu, X. Guo, C. Guo, F. Sun, C. Li, A. Pfadler, H. Zhao, B. Zhao, Pog: Personalized outfit generation for fashion recommendation at alibaba ifashion, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 2662–2670. Dataset available in: https://github.com/wenyuer/POG

  146. Z. Cui, Z. Li, S. Wu, X.-Y. Zhang, L. Wang, Dressing as a whole: Outfit compatibility learning based on node-wise graph neural networks, in The World Wide Web Conference, 2019, pp. 307–317

    Google Scholar 

  147. W.-L. Hsiao, K. Grauman, Learning the latent “look”: unsupervised discovery of a style-coherent embedding from fashion images, in 2017 IEEE International Conference on Computer Vision (ICCV) (IEEE, New York, 2017), pp. 4213–4222

    Book  Google Scholar 

  148. ShopLook: Outfits Inspirations. https://shoplook.io/home, 2020. Accessed: 2020

  149. Z. Liu, P. Luo, S. Qiu, X. Wang, X. Tang, DeepFashion: powering robust clothes recognition and retrieval with rich annotations, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. Dataset available in: http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html. Accessed: 2020

  150. Y. Ge, R. Zhang, X. Wang, X. Tang, P. Luo, Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019. Dataset available in: https://github.com/switchablenorms/DeepFashion2. Accessed: 2020

  151. ModCloth, an American online retailer of indie and vintage-inspired women’s clothing. https://www.modcloth.com, 2020. Accessed: 2020

  152. RentTheRunway, an online service that provides designer dress and accessory rentals. https://www.renttherunway.com/, 2020. Accessed: 2020

  153. W.-L. Hsiao, K. Grauman, Vibe: dressing for diverse body shapes, in Computer Vision and Pattern Recognition, 2020. Dataset available in: https://github.com/facebookresearch/VIBE. Accessed: 2020

  154. Birdsnest, an Australian women’s online fashion retailer. https://www.birdsnest.com.au/, 2020. Accessed: 2020

  155. M. Hadi Kiapour, X. Han, S. Lazebnik, A.C. Berg, T.L. Berg, Where to buy it: matching street clothing photos in online shops. in International Conference on Computer Vision, 2015. Dataset available in http://www.tamaraberg.com/street2shop. Accessed: 2020

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Authors and Affiliations

  1. KTH - Royal Institute of Technology, Stockholm, Sweden

    Shatha Jaradat & Nima Dokoohaki

  2. Spotify, Amsterdam, The Netherlands

    Humberto Jesús Corona Pampín

  3. Zalando SE, Berlin, Germany

    Reza Shirvany

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  1. Shatha Jaradat
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  2. Nima Dokoohaki
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  4. Reza Shirvany
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Correspondence to Nima Dokoohaki .

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Jaradat, S., Dokoohaki, N., Pampín, H.J.C., Shirvany, R. (2022). Fashion Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_26

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