Revolutionizing Fashion: Fashion Era’s Deep Convolutional Neural Network for Outfit Recommendations


  • Vipul V. Bag Professor, N K Orchid College of Engineering and Technolgy, Solapur MH India
  • Mithun B. Patil N K Orchid College of Engineering and Technolgy, Solapur MH India
  • Vitteshkumar D. Gaikwad N K Orchid College of Engineering and Technolgy, Solapur MH India
  • Ramanand Mohare N K Orchid College of Engineering and Technolgy, Solapur MH India
  • Sandeep P. Abhang Professor, CSMSS, Ch. Shahu College of Engineering, Aurangabad
  • Sonali Antad Asst. Professor, Vishwakarma Institute of Technology, Pune


Outfit Recommendation System, Convolutional Neural Network (CNN), IBM Assistant, Deep Learning


This article introduces a novel outfit recommendation system that leverages the user's physical appearance to provide tailored outfit suggestions. Employing a Deep Convolutional Neural Network (CNN) algorithm, the system accurately identifies the user's skin tone from input images, complemented by user-provided information like gender, age group, and size to curate personalized fashion choices aligned with the individual's body shape. The system's robustness is demonstrated through a 92% accuracy rate following model training, indicating its reliability in providing tailored suggestions. To facilitate user engagement, the system seamlessly integrates with IBM Assistant and NGROK application for efficient collection of user preferences and feedback. Looking forward, the system's roadmap includes an expansion to encompass all six types of skin tone identification based on the Fitzpatrick system. Moreover, plans involve integrating augmented reality for immersive try-on experiences, enhancing the user's interaction with suggested outfits. Additionally, the incorporation of audio chatbots aims to further optimize user convenience and engagement within the system, promising an enriched and personalized outfit recommendation experience.


Download data is not yet available.


Li, Z. (2018). Collaborative Filtering Recommendation Algorithm based on Clustering in International Journal of Performability Engineering.

Wu, Q., Zhao, P., & Cui, Z. (2020) Visual and Textual Jointly Enhanced Interpretable Fashion Recommendation in IEEE Access, 8, 68736–68746.

[3]. KHALID, M., KEMING, M., & HUSSAIN, T. (2021, December 8) Design and implementation of clothing fashion style recommendation system using deep learning in Revista Română De Informatică Și Automatică, 31(4), 123–136.

[4]. Zhang, J., Terveen, L. and Dunne, L.E. (2021), ‘The ensemble-building challenge for fashion recommendation: Investigation of in-home practices and assessment of garment combinations’ in Lecture Notes in Electrical Engineering, pp. 101–116. doi:10.1007/978-3-030-66103-8_6.

Zheng, H., Wu, K., Park, J.-H., Zhu, W., Luo, Personalized fashion recommendation from personal social media data: An item-to-set metric learning approach 2021 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata52589.2021.9671563.

Türker, N., Büyükkaplan, U. S., Kürkçüoğlu, I., & Yılmaz, B. Use of a new skin colour measurement method for the investigation of relationship between skin and tooth colour (2020, August 13) in European Oral Research, 81–85.

Garude, D., Khopkar, A., Dhake, M., Laghane, S., & Maktum, T. (2019) Skin-Tone and Occasion Oriented Outfit Recommendation System in SSRN Electronic Journal.

Wang, W., Nagai, Y., Fang, Y., & Maekawa, M. (2018, June 4) Interactive technology embedded in fashion emotional design in International Journal of Clothing Science and Technology, 30(3), 302–319.

Mengna, G., & Kuzmichev, V. E. (2013, September 30) Pressure and comfort perception in the system “female body–dress in Autex Research Journal, 13(3), 71–78.

Panda, D., Chakladar, D. D., Rana, S., & Parayitam, S. (2024). An EEG-based neuro-recommendation system for improving consumer purchase experience. Journal of Consumer Behaviour, 23(1), 61–75.

Dantone, M., Gall, J., Leistner, C., & Van Gool, L. Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images in IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11), 2131–2143.

Wang, J., & Li, Z. (2018, July). Research on Face Recognition Based on CNN in IOP Conference Series: Earth and Environmental Science, 170, 032110.

Roy, D., Panda, P., & Roy, K. (2020, January). Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning in Int. Journal of Neural Networks, 121, 148–160.

Richey, S., & Carlin, R. E. (2018, March 5) Skin Tone and Assimilation in Social Science Quarterly Journal, 99(3), 1233–1247.

HAMOUDA, M., & BOUHLEL, M. S. (2021, March 14) Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks) in IET Image Processing.,

Bhadra, S., Aneja, K., & Mandal, S. (2022, January 24) OCR Using Convolution Neural Network in Python with Keras and TensorFlow in International Journal of Advanced Research in Science, Communication and Technology, 285–292.

Mrs.Mane Mohini S, Dr.Sangave S M , Mr. Mane P M, A Detail Survey on Deep Learning Based Outfit Recommendation System in Journal of Emerging Technologies and Innovative Research (JETIR) May 2021

Nagendra Panini Challa, Abbaraju Sai Sathwik, Jinka Chandra Kiran, Kokkula Lokesh, Venkata Sasi Deepthi, Beebi Naseeba, Smart Fashion Recommendation System using FashionNet, in EAI Endorsed Transactions on Scalable Information Systems Online doi: 10.4108/eetsis.4278

Thanet Markchom, Huizhi Liang, James Ferryman, Scalable and explainable visually-aware recommender systems, Knowledge-Based Systems, Volume 263,2023,110258, ISSN 0950-7051, (2023)

Chakraborty, Samit & Hoque, Md. Saiful & Jeem, Naimur & Biswas, Manik Chandra & Bardhan, Deepayan & Lobaton, Edgar. Fashion Recommendation Systems, Models and Methods: A Review (2021) Informatics. 8. 49. 10.3390/informatics8030049

Yashar Deldjoo, Fatemeh Nazary, Arnau Ramisa, Julian McAuley. Giovanni Pellegrini, Alejandro Bellogin, Tommaso Di Noia, A Review of Modern Fashion Recommender Systems ACM Computing SurveysVolume 56Issue 4Article No.: 87pp 1–37

Agarwal Pankaj, Vempati Sreekanth and Borar Sumit 2018. Personalizing similar product recommendations in fashion E-commerce in CoRR abs/1806.11371 (2018).

Bell Sean and Bala Kavita. 2015 Learning visual similarity for product design with convolutional neural networks in ACM Transactions on Graphics 34, 4 (2015), Article 98, 10 pages.

Chen Wen, Huang Pipei, Xu Jiaming, Guo Xin, Guo Cheng, Sun Fei, Li Chao, Pfadler Andreas, Zhao Huan, and Zhao Binqiang 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

Deldjoo Yashar, Noia Tommaso Di, Malitesta Daniele, and Merra Felice Antonio. 2021 A study on the relative importance of convolutional neural networks in visually-aware recommender systems in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3961–3967

Hyunwoo Hwangbo, Yang Sok Kim, Kyung Jin Cha, Recommendation system development for fashion retail e-commerce, Electronic Commerce Research and Applications, Volume 28, 2018, Pages 94-101, ISSN 1567-4223,




How to Cite

Bag , V. V. ., Patil, M. B. ., Gaikwad, V. D. ., Mohare, R. ., Abhang, S. P. ., & Antad, S. . (2024). Revolutionizing Fashion: Fashion Era’s Deep Convolutional Neural Network for Outfit Recommendations. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 644–650. Retrieved from



Research Article