Yashar Deldjoo , Fatemeh Nazary , Arnau Ramisa , Julian McAuley , + 3 , Giovanni Pellegrini , Alejandro Bellogin , 
Tommaso Di Noia (Less) Authors Info & ClaimsAuthors : Yashar Deldjoo , Fatemeh Nazary , Arnau Ramisa , Julian McAuley , + 3 , Giovanni Pellegrini , Alejandro Bellogin , Tommaso Di Noia (Less) Authors Info & Claims
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To manage your alert preferences, click on the button below. Manage my AlertsThe textile and apparel industries have grown tremendously over the past few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms, as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion recommender systems (RSs) can have a noticeable impact on billions of customers’ shopping experiences and increase sales and revenues on the provider side.
The goal of this survey is to provide a review of RSs that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, and explainability, among others) and type of side information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics.
G. Mohammed Abdulla, Shreya Singh, and Sumit Borar. 2019. Shop your right size: A system for recommending sizes for fashion products. In Companion of the 2019 World Wide Web Conference (WWW’19). ACM, New York, NY, 327–334. DOI:
Adewole Adewumi, Adebola Taiwo, Sanjay Misra, Rytis Maskeliunas, Robertas Damasevicius, Ravin Ahuja, and Foluso Ayeni. 2019. A unified framework for outfit design and advice. In Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, Vol. 1016. Springer, 31–41. DOI:
Pankaj Agarwal, Sreekanth Vempati, and Sumit Borar. 2018. Personalizing similar product recommendations in fashion E-commerce. CoRR abs/1806.11371 (2018). http://arxiv.org/abs/1806.11371
Charu C. Aggarwal. 2016. Ensemble-based and hybrid recommender systems. In Recommender Systems. Springer, 199–224.
Divyansh Aggarwal, Elchin Valiyev, Fadime Sener, and Angela Yao. 2018. Learning style compatibility for furniture. In Pattern Recognition. Lecture Notes in Computer Science, Vol. 11269. Springer, 552–566. DOI:
Kenan E. Ak, Ashraf A. Kassim, Joo Hwee Lim, and Jo Yew Tham. 2018. Learning attribute representations with localization for flexible fashion search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7708–7717.
Ziad Al-Halah and Kristen Grauman. 2020. From Paris to Berlin: Discovering fashion style influences around the world. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10136–10145.
Ziad Al-Halah, Rainer Stiefelhagen, and Kristen Grauman. 2017. Fashion forward: Forecasting visual style in fashion. In Proceedings of the IEEE International Conference on Computer Vision. 388–397.
Mohammed Al-Rawi and Joeran Beel. 2021. Probabilistic color modelling of clothing items. Recommender Systems in Fashion and Retail 734 (2021), 21.
Enrique Amigó, Yashar Deldjoo, Stefano Mizzaro, and Alejandro Bellogín. 2023. A unifying and general account of fairness measurement in recommender systems. Information Processing & Management 60, 1 (2023), 103115.
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2021. A study of defensive methods to protect visual recommendation against adversarial manipulation of images. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1094–1103.
Phoebe R. Apeagyei. 2010. Application of 3D body scanning technology to human measurement for clothing fit. International Journal of Digital Content Technology and Its Applications 4, 7 (2010), 58–68.
Susan P. Ashdown, Suzanne Loker, Katherine Schoenfelder, and Lindsay Lyman-Clarke. 2004. Using 3D scans for fit analysis. Journal of Textile and Apparel, Technology and Management 4, 1 (2004), 1–12.
Krisztian Balog and Filip Radlinski. 2020. Measuring recommendation explanation quality: The conflicting goals of explanations. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 329–338.
Roja Bandari, Sitaram Asur, and Bernardo Huberman. 2021. The pulse of news in social media: Forecasting popularity. Proceedings of the International AAAI Conference on Web and Social Media 6, 1 (Aug. 2021), 26–33. https://ojs.aaai.org/index.php/ICWSM/article/view/14261
Debopriyo Banerjee, Krothapalli Sreenivasa Rao, Shamik Sural, and Niloy Ganguly. 2020. BOXREC: Recommending a box of preferred outfits in online shopping. ACM Transactions on Intelligent Systems and Technology 11, 6 (2020), Article 69, 28 pages. DOI:
Sean Bell and Kavita Bala. 2015. Learning visual similarity for product design with convolutional neural networks. ACM Transactions on Graphics 34, 4 (2015), Article 98, 10 pages. DOI:
Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, and Benjamin Paul Chamberlain. 2019. Fashion outfit generation for E-commerce. arXiv preprint arXiv:1904.00741 (2019).
Anurag Bhardwaj, Vignesh Jagadeesh, Wei Di, Robinson Piramuthu, and Elizabeth F. Churchill. 2014. Enhancing visual fashion recommendations with users in the loop. CoRR abs/1405.4013 (2014). http://arxiv.org/abs/1405.4013
Christian Bracher, Sebastian Heinz, and Roland Vollgraf. 2016. Fashion DNA: Merging content and sales data for recommendation and article mapping. CoRR abs/1609.02489 (2016).
Alexander Brand and Tom Gross. 2020. Paying the pink tax on a blue dress—Exploring gender-based price-premiums in fashion recommendations. In Human-Centered Software Engineering. Lecture Notes in Computer Science, Vol. 12481. Springer, 190–198. DOI:
Ângelo Cardoso, Fabio Daolio, and Saúl Vargas. 2018. Product characterisation towards personalisation: Learning attributes from unstructured data to recommend fashion products. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). ACM, New York, NY, 80–89. DOI:
Yeongnam Chae, Jiu Xu, Björn Stenger, and Soh Masuko. 2018. Color navigation by qualitative attributes for fashion recommendation. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE’18). IEEE, Los Alamitos, CA, 1–3. DOI:
Yu-Ting Chang, Wen-Haung Cheng, Bo Wu, and Kai-Lung Hua. 2017. Fashion world map: Understanding cities through streetwear fashion. In Proceedings of the 25th ACM International Conference on Multimedia. 91–99.
Huizhong Chen, Andrew Gallagher, and Bernd Girod. 2012. Describing clothing by semantic attributes. In Proceedings of the European Conference on Computer Vision. 609–623.
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’17). ACM, New York, NY, 335–344.
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 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’19). 2662–2670.
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 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’19). 2662–2670. DOI:
Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019. 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 (SIGIR’19). ACM, New York, NY, 765–774. DOI:
Wen-Huang Cheng, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati, and Jiaying Liu. 2021. Fashion meets computer vision: A survey. ACM Computing Surveys 54, 4 (2021), 1–41.
Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, and Mohan Kankanhalli. 2019. MMALFM: Explainable recommendation by leveraging reviews and images. ACM Transactions on Information Systems 37, 2 (2019), 1–28.
Sameer Chhabra. 2017. Netflix says 80 percent of watched content is based on algorithmic recommendations. MobileSyrup. Retrieved March 13, 2021 from https://mobilesyrup.com/2017/08/22/80-percent-netflix-shows-discovered-recommendation/
Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, and Diogo Gonçalves. 2022. “Does it come in black?” CLIP-like models are zero-shot recommenders. CoRR abs/2204.02473 (2022). DOI:
Kyung-Yong Chung. 2014. Effect of facial makeup style recommendation on visual sensibility. Multimedia Tools and Applications 71, 2 (2014), 843–853. DOI:
