YAPAY ZEKA TEKNİKLERİ İLE GELİŞTİRİLEN GİYİM ÖNERİ SİSTEMİNİN UYGULANMASI

Abstract

Bu çalışmada, yoğun iş temposunda çalışan çok sayıda giysiye sahip olan ancak zamanı kısıtlı olan kullanıcılar için bir giyim önerisi uygulaması geliştirilmesi amaçlanmıştır. Bu uygulama, kullanıcıların kıyafet seçimi ve şık giyinmeye harcadıkları zamanı azaltmak ve kıyafetlerini etkili bir şekilde kullanmalarına yardımcı olmayı amaçlar. Bu uygulamanın geliştirilmesi sürecinde ilk olarak kullanıcının giyim tercihlerini etkileyen kriterler belirlendi. Daha sonra belirlenen kriterlere göre bir gardırop veri seti oluşturuldu. Daha sonra kıyafet önerme yöntemleri araştırıldı. Araştırma sonucunda bu çalışmada giyim önerisi için ilişki kuralı analizi, çok boyutlu giyim temsil kodlaması ve ağırlıklı L1 uzaklık yöntemlerinden yararlanılmasına karar verildi. Uygulama aşamasında, seçilen yöntemlerle, ilgili veri seti kullanılarak deneyler gerçekleştirildi. Bu çalışmada geliştirilen uygulamanın, kullanıcı tercihlerine uygun kıyafet önermede başarılı sonuçlar verdiği tespit edilmiştir.

Keywords

• Öneri sistemi
• giyim
• seçim kriterleri
• Yapay Zeka Teknikleri.

APPLICATION OF DEVELOPING CLOTHING RECOMMENDATION SYSTEM WITH ARTIFICIAL INTELLIGENCE TECHNIQUES

Abstract

This study aims to develop a clothing recommendation application for users who possess a large number of clothes but have limited time due to their intense work tempo. This application aims to assist them in using their clothes effectively, reducing the time spent on selecting outfits, and dressing fashionably. In the process of developing this application, firstly, the criteria influencing the user's clothing preferences were determined. Subsequently, a wardrobe dataset was created based on the established criteria. Following this, methods for suggesting clothes were explored. As a result of the research, it was decided to utilize association rule analysis, multidimensional clothing representation coding, and weighted L1 distance methods for clothing recommendation in this study. In the application phase, experiments were conducted using the dataset associated with the chosen methods. It has been determined that the application developed in this study gives successful results in suggesting clothes suitable for user preferences.

Keywords

• Recommendation system
• clothing
• selection criteria
• Artificial Intelligence Techniques.

References

1. Deng, Q., Wang, R., Gong, Z., Zheng, G., & Su, Z. (2018). Research and implementation of personalized clothing recommendation algorithm. In 2018 7th International Conference on Digital Home (ICDH), 219-223 IEEE.
2. Liu, K. H., Wang, F. & Liu, T. J. (2019). A clothing recommendation dataset for online shopping. In 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 1-2.
3. 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. In 2019 International Automatic Control Conference (CACS), 1-6.
4. Yang, B. (2022). Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning. Computational Intelligence and Neuroscience, 2022.
5. Bullón Pérez, J.J., Queiruga-Dios, A., Gayoso Martínez, V. & Martín Del Rey, Á., (2020). Traceability of Ready-to-Wear Clothing through Blockchain Technology. MDPI Sustainability 12, 7491 doi:10.3390/su12187491.
6. Hidayati, S. C., Hsu, C. C., Chang, Y. T., Hua, K. L., Fu, J. & Cheng, W. H., (2018). What dress fits me best? Fashion recommendation on the clothing style for personal body shape. the 26th ACM international conference on Multimedia, 438-446.
7. Pandit, A., Goel, K., Jain, M., & Katre, N. (2020). A review on clothes matching and recommendation systems based on user attributes. International Journal of Engineering Research & Technology, 9(8).
8. Zheng, P., & Ni, L. (2010). Smart phone and next generation mobile computing. Elsevier.

9. Zolkepli, I. A., Mukhiar, S. N. S., & Tan, C. (2021). Mobile consumer behaviour on apps usage: The effects of perceived values, rating, and cost. Journal of marketing communications, 27:6, 571-593.
10. Correia, M., Cunningham, N., & Roberts-Lombard, M. (2023). Use of mobile apps when purchasing apparel: A young male adult perspective. South African Journal of Information Management, 25:1, 1-14.
11. Shrestha, P. (2023, June 17), Üç grafik ile Türkiye: Kurulumlar, oturumlar ve kullanıcı tutma oranları, [Press release], https://www.adjust.com/tr/blog/the-state-of-mobile-apps-in-turkey-2023/
12. Yang, T., Man, X., Feng, J., Chen, J. & Tao, R. (2018). Research on color expansion method in clothing recommendation system. In 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), 482-487 (a)
13. Yang, Z., Su, Z., Yang, Y. & Lin, G. (2018). From recommendation to generation: A novel fashion clothing advising framework. In 2018 7th International Conference on Digital Home (ICDH), 180-186 (b)
14. Suresh, S., Kumar, A. & Reddy, S. (2019). Fashion recommendation system using association rule mining. Journal of Fashion Technology and Industry 12(2), 1-10.
15. Zhang, X., Jun, M. & Xuefei, Z. (2021). Fashion Recommendation Systems Using Multimodal Data. Transactions on Knowledge and Data Engineering 33:1, 269-282.
16. Chen, Y., Yan Z. & Wei, W., (2021). Fashion Recommendation Systems Using Context. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17.1, 1-24.
17. Wu, S., Tang, Y. & Wang, L. (2020). Fashion style recommendation by exploiting user-item, item-item, and user-user similarities. Knowledge-Based Systems 198, 105696.
18. Wang, W., Chen, J. & Li, X. (2021). Fashion recommendation systems: A new perspective. ACM Transactions on Information Systems 39(2), 1-25.
19. Wu, W., Chen, K. & Zheng, W.S. (2021). Fashion compatibility prediction by adversarial graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 33(1), 118-131.
20. Wen, Y., Liu, X. & Xu, B. (2018). Personalized clothing recommendation based on knowledge graph. In 2018 International Conference on Audio, Language and Image Processing (ICALIP), 1-5.
21. Jain, M., Singh, S., Chandrasekaran, K., Rathnamma, M. V. & Ramana, V. V. (2020). Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), 1-6.
22. Park, C. Y., Lim, B. C., Lee, W. J., Lee, C. S., Kim, M. S. & Lee, S. Y. (2022). Suitable clothing recommendation system by size and skin color. Journal of Digital Convergence 20:3, 407-413.
23. Atina, V. & Hartanti, D., (2022). Knowledge Based Recommendation Modeling for Clothing Product Selection Recommendation System. Jurnal Teknik Informatika (Jutif) 3:5, 1407-1413.
24. Khalid, M., Keming, M. & Hussain, T. (2021). Design and implementation of clothing fashion style recommendation system using deep learning. Rom J Inform Technol Autom Control 31:4, 123-136.
25. Liu, Y., Gao, Y., Feng, S. & Li, Z. (2017). Weather-to-clothes: Weather-oriented clothing recommendation. In 2017 IEEE International Conference on Multimedia and Expo (ICME), 181-186.
26. Woiceshyn, L., Wang, Y., Nejat, G. & Benhabib, B. (2017). Personalized clothing recommendation by a social robot. In 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), 179-185.
27. Wang, R., Wang, J. & Su, Z. (2022). Learning compatibility knowledge for outfit recommendation with complementary clothing matching. Computer Communications 181, 320-328.
28. Arunkumar, S., Deepak, G., Priyadarshini, J. S. & Santhanavijayan, A. (2023). PMFRO: Personalized men’s fashion recommendation using dynamic ontological models. Hybrid Intelligent Systems 96-105.
29. Ibaydullaev, T. G. (2020). Philosophical and cultural foundations of the classification and genesis of clothes. Theoretical & Applied Science 84:4, 754-757.
30. Bedez Üte, T., Çelik, P., Kadoğlu, H., Üzümcü, M. B., Ertekin, G. & Marmarali, A., (2018). An Investigation on the Use of Different Natural Fibres in Undergarments in Terms of Comfort Properties, Journal of Textiles and Engineer, 25: 112, 335-343.
31. Dönmez, E.T. (2008). An Investigation on the Factors Affecting the Unit Fabric Weight of Circular Knitted Fabrics, [Unpublished master’s thesis], Dokuz Eylül University.
32. Cheng, C. I., Liu, D. S. M., Liu, M. L., & Wan, I. E. (2014). Clothing matchmaker: automatically finding apposite garment pairs from personal wardrobe. International Journal of Organizational Innovation, 7:1, 78.
33. Rocha, D., Carvalho, V., Soares, F., & Oliveira, E. (2021). A model approach for an automatic clothing combination system for blind people. In Design, Learning, and Innovation: 5th EAI International Conference, December 10-11, 2020, Proceedings 5, 74-85, Springer International Publishing.
34. Jia, D. (2022). January. Intelligent Clothing Matching Based on Feature Analysis. In 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 653-656.
35. Erkilet, A. (2012). Transformation of Privacy: “Islamic” Fashion Magazines in the Context Value, Imitation and Conspicuous Consumption. Individual and Society Journal of Social Science, 2:2, 27-40.
36. Polat, G., & Arslan, H. Y. (2015). People’s Views on Women Who Wears Pants Related to Religious Perspective. Akademik Bakış Uluslararası Hakemli Sosyal Bilimler Dergisi, 47, 63-70.
37. Li, J., Xia, S., West, A. J., & Istook, C. L. (2022). Fashionable sportswear working as a body measurement collecting tool. International Journal of Clothing Science and Technology, 34:4, 589-604.
38. Kashilani, D., Damahe, L. B., & Thakur, N. V. (2018). August. An overview of image recognition and retrieval of clothing items. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), 1-6.
39. Kang, T. (2013). Wearther: An iOS Mobile Application for Women. [Unpublished master’s thesis], Rochester Institute of Technology
40. Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. Knowledge discovery in database, 229-248.
41. Altuntaş, V., (2020). Birliktelik Kural Analizi Tabanlı İzleme ve Bayes Ağları ile Operasyonel Teknoloji Sistemlerinde Siber Güvenlik Analizi. Avrupa Bilim ve Teknoloji Dergisi 20, 498-505.
42. Agrawal, R., Imieliński, T.& Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 207-216.
43. Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th int.conf. very large data bases, VLDB 1215, 487-499.
44. Altuntas, V., Gök, M. & Kahveci, T. (2018). Stability analysis of biological networks’ diffusion state. IEEE/ACM transactions on computational biology and bioinformatics 17:4, 1406-1418.