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Choosing the Right Model: A Comprehensive Analysis of Outfit Recommendation Systems

by Gursimran Kaur, Hrithik Malhotra, Tanmaya Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 12
Year of Publication: 2021
Authors: Gursimran Kaur, Hrithik Malhotra, Tanmaya Gupta
10.5120/ijca2021921413

Gursimran Kaur, Hrithik Malhotra, Tanmaya Gupta . Choosing the Right Model: A Comprehensive Analysis of Outfit Recommendation Systems. International Journal of Computer Applications. 183, 12 ( Jun 2021), 13-20. DOI=10.5120/ijca2021921413

@article{ 10.5120/ijca2021921413,
author = { Gursimran Kaur, Hrithik Malhotra, Tanmaya Gupta },
title = { Choosing the Right Model: A Comprehensive Analysis of Outfit Recommendation Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 12 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number12/31978-2021921413/ },
doi = { 10.5120/ijca2021921413 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:35.791150+05:30
%A Gursimran Kaur
%A Hrithik Malhotra
%A Tanmaya Gupta
%T Choosing the Right Model: A Comprehensive Analysis of Outfit Recommendation Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 12
%P 13-20
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This Survey unravels developmental research on Fashion Recommendation Systems. (FRS). There is an introduction to the the three types of Recommendation Systems that are present: Content based, Collaborative Filtering and Hybrid Models, and a discussion on their pros and cons. Then onto discussing the challenges faced by Recommendation approaches followed by specifically the ones by Fashion Recommendation Systems. The need for presenting Outfit recommendation models and the importance of their accuracy is presented. Finally, a comprehensive survey of 4 types of Fashion Recommendation Systems: 1.) Collaborative Filtering 2.) Content based 3.) Hybrid 4.) Ontology based. A presentation of these with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges is carried out.

References
  1. S. Sharma, L. Koehl, P. Bruniaux and X. Zeng, ”Garment Fashion Recommendation System for Customized Garment”, 2019 International Conference on Industrial Engineering and Systems Management (IESM),pp. 1-6, 2019, Accessed on: May 7, 2021. [Online]. Available doi: 10.1109/IESM45758.2019.8948164.
  2. Y. Lin et al., ”Clothing Recommendation System based on Visual Information Analytics”, 2019 International Automatic Control Conference (CACS), 2019.
  3. Q. Tu and L. Dong, ”An Intelligent Personalized Fashion Recommendation System”, 2010 International Conference on Communications, Circuits and Systems (ICCCAS), 2010. pp. 479-485, Accessed on: May 7, 2021. [Online]. Available doi: 10.1109/ICCCAS.2010.5581949.
  4. L. Yu-Chu, Y. Kawakita, E. Suzuki and H. Ichikawa, ”Personalized Clothing-Recommendation System Based on a Modified Bayesian Network”, 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet, 2012.
  5. N. Ramesh and T. Moh, ”Outfit Recommender System”, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018.
  6. J. J. Fernandes, R. Fernandes, ”A study of fashion recommender systems with hybrid collaborative filtering & fuzzy logic”, International Journal of Latest Trends in Engineering and Technology Special Issue, SACAIM, pp. 132-137, 2017.
  7. A. Kolstad, O¨ . O¨ zgo¨bek, J. A. Gulla and S. Lillehammer, “Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits”, RecSysKTL, pp. 279-289, 2017.
  8. N. Ramesh and T. Moh, ”Outfit Recommender System”, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018.
  9. Xi. Hu, W. Zhu, Q. Li, “HCRS: A hybrid clothes recommender system based on user ratings and product features”, unpublished paper, Sch. of Economic Inf. Eng. Southwestern Univ. of Finance & Econ. Chengdu, China 2014.
  10. Y. Huang and T. Huang, ”Outfit Recommendation System Based on Deep Learning”, Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017), 2017.
  11. V. Jagadeesh, R. Piramuthu, A. Bhardwaj, W. Di and N. Sundaresan, “Large scale visual recommendations from street fashion images”, in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, vol 32, pp. 67-75, 2014.
  12. S. Liu et al., ”Hi, magic closet, tell me what to wear!”, Proceedings of the 20th ACM international conference on Multimedia - MM ’12, 2012.
  13. H. Yang, X. Yi and L. S. Davis, “Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach.” Proceedings of the 23rd ACM international conference on Multimedia, pp. 56-79, 2015.
  14. Usip, Patience & Osang, Francis & Konyeha, Susan, “An Ontology-Driven Fashion Recommender System for Occasion-Specific Apparels” pp. 67-76, March 2020, Accessed on: May 7, 2021. [Online]. Available doi: 10.22624/aims/mathscompv8n1p6
  15. W. Yang, P. Luo and L. Lin, ”Clothing Co-parsing by Joint Image Segmentation and Labeling”, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
  16. W. Zei, Z. Yanghoung, L. Runze and M. P. Y, “Fashion Recommendations using Text Mining and Multiple Content Attributes”, in 25th International Conference in Central Europe on Computer Graphics, pp. 47-52, 2017.
  17. L. Bossard, M. Dantone, C. Leistner, C. Wengert, T. Quack and L. V. Gool, in “Apparel Classification with Style.” in: “Asian Conference on Computer Vision ACCV 2012: Computer Vision – ACCV”,vol. 7727, pp. 321-335, 2012. Accessed on: May 8, 2021. [Online]. Available doi: https://doi.org/10.1007/978-3-642-37447-025
  18. L. C. Wang, X. Y. Zeng, L. Koehl and Y. Chen, ”Intelligent Fashion Recommender System: Fuzzy Logic in Personalized Garment Design,” in IEEE Transactions on Human-Machine Systems, vol. 45, no. 1, pp. 95-109.
  19. S. Liu, L. Liu and S. Yan, ”Magic Mirror: An Intelligent Fashion Recommendation System”, 2013 2nd IAPR Asian Conference on Pattern Recognition, pp. 278-290, 2013.
  20. X. Zeng, Y. Zhu, L. Koehl, M. Camargo, C. Fonteix and F. Delmotte, ”A fuzzy multi-criteria evaluation method for designing fashion oriented industrial products”, Soft Computing, vol. 14, no. 12, pp. 1277-1285, 2009.
  21. E. Viriato de Melo, E. A. Nogueira and D. Guliato, ”Content- Based Filtering Enhanced by Human Visual Attention Applied to Clothing Recommendation,” 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 644-651, 2015.
  22. Q. Deng, R. Wang, Z. Gong, G. Zheng and Z. Su, ”Research and Implementation of Personalized Clothing Recommendation Algorithm”, 2018 7th International Conference on Digital Home (ICDH), pp. 345-367, 2018
  23. Liu Y., Nie J., Xu L., Chen Y., Xu B., “Clothing Recommendation System Based on Advanced User-Based Collaborative Filtering Algorithm. In: Sun S., Chen N., Tian T. (eds) Signal and Information Processing, Networking and Computers,” ICSINC 2017.
  24. Y. Liu, J. Nie, L. Xu, Y. Chen and B. Xu, ”Clothing Recommendation System Based on Advanced User-Based Collaborative Filtering Algorithm”, Lecture Notes in Electrical Engineering, pp. 436-443, 2017.
Index Terms

Computer Science
Information Sciences

Keywords

Recommendation System Fashion Collaborative Filtering Content-based Filtering Hybrid Filtering Ontology Deeplearning Visual Features

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