Authors : Harisu Aliyu; Abdulamalik Abdulsalam; Jamilu Musa; Grace Ojochenemi Emmanuelanorue; Umar Muhammad Bello; Paul Joseph Agada; Ahmad Abubakar Yusuf; Abdullahi Lawal Rukuna

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/2yn5puzb

Scribd : https://tinyurl.com/2w4p2dkd

DOI : https://doi.org/10.38124/ijisrt/25dec1676

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid digital transformation of the fashion industry has amplified the need for intelligent tools that can navigate vast product catalogs and deliver personalized recommendations to consumers. Fashion recommender systems, leveraging machine learning and deep learning techniques, have emerged as a crucial solution to address challenges such as choice overload, style compatibility, and changing trends. This review examines the evolution of fashion recommender systems, from early content-based and collaborative filtering approaches to hybrid, context-aware, and deep learning-based models capable of processing multimodal data. The methodology involved a structured literature search across major academic databases, focusing on recent studies that directly address clothing and fashion recommendation. Key findings reveal that while significant progress has been made in personalization accuracy, diversity, and visual understanding, persistent limitations remain. These include reliance on small or proprietary datasets, lack of demographic and cultural diversity, inconsistent evaluation protocols, and domain shifts between curated catalog images and real-world contexts. Addressing these challenges will require the development of standardized, diverse benchmark datasets, transparent experimental reporting, and the integration of ethical considerations such as fairness, inclusivity, and privacy. This paper provides a comprehensive synthesis of existing research, identifies current gaps, and outlines future directions for building robust, contextually aware, and user-centered fashion recommender systems.

Keywords : Content-Based Filtering, Collaborative Filtering, Hybrid Recommendation Models, Deep Learning, Machine Learning.

References :

1. Abugabah, A., Cheng, X., & Wang, J. (2020). Learning Context-Aware Outfit Recommendation. Symmetry, 12(6), 873. https://doi.org/10.3390/sym12060873
2. ASIROGLU, B., ATALAY, M. I., BALKAYA, A., TUZUNKAN, E., Dagtekin, M., & ENSARI, T. (2019). Smart Clothing Recommendation System with Deep Learning. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 1–4. https://doi.org/10.1109/ISMSIT.2019.8932738
3. Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487–1524. https://doi.org/10.3233/IDA-163209
4. Chris, E., Joe, E., & Grace, A. (2024). Metadata in Recommender Systems. https://www.researchgate.net/publication/385774403
5. C.S., D., R, K., & N, S. (2024). Enhancing E-Commerce with Personalized Product Recommendations. https://doi.org/10.20944/preprints202410.2506.v1
6. Deldjoo, Y., Nazary, F., Ramisa, A., McAuley, J., Pellegrini, G., Bellogin, A., & Noia, T. Di. (2024). A Review of Modern Fashion Recommender Systems. ACM Computing Surveys, 56(4), 1–37. https://doi.org/10.1145/3624733
7. Elahi, M., Khosh Kholgh, D., Kiarostami, M. S., Oussalah, M., & Saghari, S. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Sciences, 625, 738–756. https://doi.org/10.1016/j.ins.2023.01.051
8. Eldemerdash, D., AL shikh, K., & Abou-Ghali, M. (2023). Fashion Recommendation System and its Impact on Consumers’ Purchase Decision Making. International Design Journal, 13(1), 195–206. https://doi.org/10.21608/idj.2023.276188
9. Fareed, A., Hassan, S., Belhaouari, S. B., & Halim, Z. (2023). A collaborative filtering recommendation framework utilizing social networks. Machine Learning with Applications, 14, 100495. https://doi.org/10.1016/j.mlwa.2023.100495
10. Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Applied Sciences, 10(21), 7748. https://doi.org/10.3390/app10217748
11. Gazzola, P., Pavione, E., Pezzetti, R., & Grechi, D. (2020). Trends in the Fashion Industry. The Perception of Sustainability and Circular Economy: A Gender/Generation Quantitative Approach. Sustainability, 12(7), 2809. https://doi.org/10.3390/su12072809
12. Guan, C., Qin, S., Ling, W., & Ding, G. (2016). Apparel recommendation system evolution: an empirical review. International Journal of Clothing Science and Technology, 28(6), 854–879. https://doi.org/10.1108/IJCST-09-2015-0100
13. Hara, K., Jagadeesh, V., & Piramuthu, R. (2016). Fashion apparel detection: The role of deep convolutional neural network and pose-dependent priors. 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 1–9. https://doi.org/10.1109/WACV.2016.7477611
14. Haruna, K., Akmar Ismail, M., Suhendroyono, S., Damiasih, D., Pierewan, A., Chiroma, H., & Herawan, T. (2017). Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction. Applied Sciences, 7(12), 1211. https://doi.org/10.3390/app7121211
15. Hou, M., Wu, L., Chen, E., Li, Z., Zheng, V. W., & Liu, Q. (2019). Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach. http://vision.cs.utexas.edu/projects/finegrained/utzap50k/
16. Jain, V., & Wah, C. (2022). Computer Vision in Fashion Trend Analysis and Applications. Journal of Student Research, 11(1). https://doi.org/10.47611/jsrhs.v11i1.2464
17. Joshi, A., Wong, C. L., Marinho de Oliveira, D., Zafari, F., Mourao, F., Ribas, S., & Pandey, S. (2022). Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative Filtering. Proceedings of the 16th ACM Conference on Recommender Systems, 531–533. https://doi.org/10.1145/3523227.3547404
18. Kang, L., & Wang, Y. (2024). Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm. Heliyon, 10(3), e25044. https://doi.org/10.1016/j.heliyon.2024.e25044
19. Kawasaki, M., & Hasuike, T. (2017). A recommendation system by collaborative filtering including information and characteristics on users and items. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8280983
20. Khan, A., Ahmad, A., Rahman, A. U., & Alkhalil, A. (2020). A Mobile Cloud Framework for Context-Aware and Portable Recommender System for Smart Markets (pp. 283–309). https://doi.org/10.1007/978-3-030-13705-2_12
21. Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2020). A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies, 25(4), 2635–2664. https://doi.org/10.1007/s10639-019-10063-9
22. Lee, S., & Kim, D. (2022). Deep learning based recommender system using cross convolutional filters. Information Sciences, 592, 112–122. https://doi.org/10.1016/j.ins.2022.01.033
23. Lin, Y.-R., Su, W.-H., Lin, C.-H., Wu, B.-F., Lin, C.-H., Yang, H.-Y., & Chen, M.-Y. (2019). Clothing Recommendation System based on Visual Information Analytics. 2019 International Automatic Control Conference (CACS), 1–6. https://doi.org/10.1109/CACS47674.2019.9024361
24. Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32. https://doi.org/10.1016/j.dss.2015.03.008
25. Madanchian, M. (2024). The Impact of Artificial Intelligence Marketing on E-Commerce Sales. Systems, 12(10), 429. https://doi.org/10.3390/systems12100429
26. Mateos, P., & Bellogín, A. (2024). A systematic literature review of recent advances on context-aware recommender systems. Artificial Intelligence Review, 58(1), 20. https://doi.org/10.1007/s10462-024-10939-4
27. Mohamed, M. H., Khafagy, M. H., & Ibrahim, M. H. (2019). Recommender Systems Challenges and Solutions Survey. 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), 149–155. https://doi.org/10.1109/ITCE.2019.8646645
28. Mswahili, M. E., & Jeong, Y.-S. (2024). Transformer-based models for chemical SMILES representation: A comprehensive literature review. Heliyon, 10(20), e39038. https://doi.org/10.1016/j.heliyon.2024.e39038
29. Nguyen, D.-N., Nguyen, V.-H., Trinh, T., Ho, T., & Le, H.-S. (2024). A personalized product recommendation model in e-commerce based on retrieval strategy. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100303. https://doi.org/10.1016/j.joitmc.2024.100303
30. Permana, K. E. (2024). Comparison of User Based and Item Based Collaborative Filtering in Restaurant Recommendation System. Mathematical Modelling of Engineering Problems, 11(7), 1922–1928. https://doi.org/10.18280/mmep.110723
31. Ponce, V., & Abdulrazak, B. (2022). Context-Aware End-User Development Review. Applied Sciences, 12(1), 479. https://doi.org/10.3390/app12010479
32. Pralhad Magar, S., & Kondvilkar, S. (2020). MODA-A fashion community and outfit recommender Android application. https://www.researchgate.net/publication/342154496
33. Ramesh, N., & Moh, T.-S. (2018). Outfit Recommender System. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 903–910. https://doi.org/10.1109/ASONAM.2018.8508656
34. Sabiri, B., Khtira, A., El Asri, B., & Rhanoui, M. (2025). Hybrid Quality-Based Recommender Systems: A Systematic Literature Review. Journal of Imaging, 11(1), 12. https://doi.org/10.3390/jimaging11010012
35. Sarojadevi, H., Bhat, V., Palekar, R., & Neha, K. (2023). Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit. Asian Journal of Computer Science and Technology, 12(1), 21–24. https://doi.org/10.51983/ajcst-2023.12.1.3569
36. Sawalkar, P., Udar, B., Bobade, A., Gupta, N., & Kahar, S. (2023). FASHION RECOMMENDATION SYSTEM. JETIR. www.jetir.orgm650
37. Sharad Phalle, Ms. T., & Bhushan, Prof. S. (2024). Content Based Filtering And Collaborative Filtering: A Comparative Study. Journal of Advanced Zoology, 96–100. https://doi.org/10.53555/jaz.v45iS4.4158
38. Shirkhani, S., Mokayed, H., Saini, R., & Chai, H. Y. (2023). Study of AI-Driven Fashion Recommender Systems. SN Computer Science, 4(5), 514. https://doi.org/10.1007/s42979-023-01932-9
39. Shushi, A., & Adnan M. Abdulazeez. (2024). Fashion Design Classification Based on Machine Learning and Deep Learning Algorithms: A Review. Indonesian Journal of Computer Science, 13(3). https://doi.org/10.33022/ijcs.v13i3.3980
40. Sivaranjani, L., Rachamadugu, S. K., Reddy, B. V. S., A, B. R., Sakthivel, M., & Depuru, S. (2023). Fashion Recommendation System Using Machine Learning. 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), 1367–1374. https://doi.org/10.1109/ICOSEC58147.2023.10275967
41. Sridevi, M., ManikyaArun, N., Sheshikala, M., & Sudarshan, E. (2020). Personalized fashion recommender system with image based neural networks. IOP Conference Series: Materials Science and Engineering, 981, 022073. https://doi.org/10.1088/1757-899X/981/2/022073
42. Suvarna, B., & Balakrishna, S. (2022). An Efficient Fashion Recommendation System using a Deep CNN Model. 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), 1179–1183. https://doi.org/10.1109/ICACRS55517.2022.10029063
43. Suvarna, B., & Balakrishna, S. (2024). Enhanced content-based fashion recommendation system through deep ensemble classifier with transfer learning. Fashion and Textiles, 11(1), 24. https://doi.org/10.1186/s40691-024-00382-y
44. Syiam, M. K., Wibowo, A. T., & Setiawan, E. B. (2023). Fashion Recommendation System using Collaborative Filtering. Building of Informatics, Technology and Science (BITS), 5(2). https://doi.org/10.47065/bits.v5i2.3690
45. Thakur, S. (2024). Enhancing Outfit Recommendation with a CNN-kNN Hybrid Model and Digital Wardrobe Management. International Journal for Research in Applied Science and Engineering Technology, 12(4), 2463–2467. https://doi.org/10.22214/ijraset.2024.60378
46. Wang, C.-D., Deng, Z.-H., Lai, J.-H., & Yu, P. S. (2019). Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering. IEEE Transactions on Cybernetics, 49(7), 2678–2692. https://doi.org/10.1109/TCYB.2018.2841924
47. Yarahmadi Gharaei, N., Dadkhah, C., & Daryoush, L. (2021). Content-based Clothing Recommender System using Deep Neural Network. 2021 26th International Computer Conference, Computer Society of Iran (CSICC), 1–6. https://doi.org/10.1109/CSICC52343.2021.9420544
48. Ye, T., Hu, L., Zhang, Q., Lai, Z. Y., Naseem, U., & Liu, D. D. (2023). Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System. Proceedings of the ACM Web Conference 2023, 1172–1180. https://doi.org/10.1145/3543507.3583435
49. Yoon, J. H., & Jang, B. (2023). Evolution of Deep Learning-Based Sequential Recommender Systems: From Current Trends to New Perspectives. IEEE Access, 11, 54265–54279. https://doi.org/10.1109/ACCESS.2023.3281981
50. Yuan, H., & Hernandez, A. A. (2023). User Cold Start Problem in Recommendation Systems: A Systematic Review. IEEE Access, 11, 136958–136977. https://doi.org/10.1109/ACCESS.2023.3338705
51. Zhu, Z. (2023). The Impact of Digital Transformation on the Fashion Industry in the Post-Pandemic Era. BCP Business & Management, 44, 696–705. https://doi.org/10.54691/bcpbm.v44i.4920