A Review on Outfit Fashion Recommendation System

Authors

  • Bhagyshree Pravin Bhure  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Pratiksha Tulshiram Bansod  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Monali Shivram Amgaokar  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Savita Pralhad Lodiwale  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Anjali Pravin Orkey  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Ashish Mohod  Assistant Professor, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India

Keywords:

CNN, BLOB, EDAS, GTSRB

Abstract

With the quick rise in living standards, people's shopping passion grew, and their desire for clothing grew as well. A growing number of people are interested in fashion these days. However, when confronted with a large number of garments, consumers are forced to try them on multiple times, which takes time and energy. As a result of the suggested Fashion Recommendation System, a variety of online fashion businesses and web applications allow buyers to view collages of stylish items that look nice together. Clients and sellers benefit from such recommendations. On the one hand, customers can make smarter shopping decisions and discover new articles of clothes that complement one other. Complex outfit recommendations, on the other hand, assist vendors in selling more products, which has an impact on their business. FashionNet is made up of two parts: a feature network for extracting features and a matching network for calculating compatibility. A deep convolutional network is used to achieve the former. For the latter, a multi-layer completely connected network topology is used. For FashionNet, you must create and compare three different architectures. To achieve individualised recommendations, a two-stage training technique was created.

References

  1. J. McAuley, C. Targett, Q. Shi, and A. van den Hengel, “Image-based recommendations on styles and substitutes,” in Proceedings of the International ACM Conference on Research and Development in Information Retrieval (SIGIR), 2015.
  2. Y. Hu, X. Yi, and L. S. Davis, “Collaborative fashion recommendation: A functional tensor factorization approach,” in Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, 2015.
  3. J. L. D. Thombre, D.V.; Nirmal, “Human detection and tracking using image segmentation and kalman filter,” Intelligent Agent Multi-Agent Systems, pp. 1 - 5, 2009.
  4. A. Veit, B. Kovacs, S. Bell, J. McAuley, K. Bala, and S. Belongie, “Learning visual clothing style with heterogeneous dyadic co-occurrences,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015.
  5. J. H.-J. J. R. G. Orrite-Urunuela, C.; del Rincon, “2d silhouette and 3d skeletal models for human detection and tracking,” 17th International Conference on Pattern Recognition, vol. 4, pp. 244 - 247, 2004.
  6. A. J. W. W.-Y. Y. L. J. How-Lung Eng; Kam, “Human detection and tracking within hostile aquatic environments,” 12th International Conference on Image Analysis and Processing, pp. 133 - 138, 2003.
  7. S.Ajmani, H. Ghosh, A.Mallik, and S.Chaudhury, “An ontology based personalized garment recommendation system,” in Proceedings of the IEEE/WIC/ACM International Joint Conference onWeb Intelligence and Intelligent Agent TechnologyWorkshops, WI-IATW2013, pp. 17-20, November 2013.
  8. H. Li, F. Cai, and Z. Liao, “Content-Based Filtering Recommendation Algorithm Using,” in Proceedings of the HMM, pp. 275- 277, 2012.
  9. E. A. Nogueira, E. V. De Melo, E. R. De Faria, and D. Guliato, “IKB-MS: A collaborative filtering approach associated with human visual attention for clothing recommendation,” in Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, WebMedia 2015, pp. 149-156, October 2015.
  10. I. Hwang, M. Kim, and H. J. Ahn, “Data pipeline for generation and recommendation of the IoT rules based on open text data,” in Proceedings of the 30th IEEE International Conference on Advanced Information Networking and ApplicationsWorkshops, WAINA 2016, pp. 238-242, March 2016.
  11. M. Scholz, V. Dorner, M. Franz, and O. Hinz, “Measuring consumers’ willingness to pay with utility-based recommendation systems,” Decision Support Systems, vol. 72, pp. 60-71, 2015.
  12. N. Koenig, “Toward real-time human detection and tracking in diverse environments,” IEEE 6th International Conference on Development and Learning, pp. 94 - 98, 2007.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bhagyshree Pravin Bhure, Pratiksha Tulshiram Bansod, Monali Shivram Amgaokar, Savita Pralhad Lodiwale, Anjali Pravin Orkey, Ashish Mohod, " A Review on Outfit Fashion Recommendation System, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.220-222, May-June-2021.