An Online Recommendation System Using Deep Learning for Textile Products

Turkut, Umit; Tuncer, Adem; Savran, Hüseyin; Yılmaz., Sait

doi:10.1109/hora49412.2020.9152875

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Recently, many researchers tried to explore and provide solutions for different fashion tasks using the concepts of artificial intelligence. Several works contributed for fashion recommendation in [20,80], object detection and classification [37,43,44,83,95], Image [65] and ClothCap [101] Generation and Manipulation in [17,67,70]. 17 illustrates an overview of the AI application in the field of Fashion.…”

Section: Applicationmentioning

confidence: 99%

You can try without visiting: a comprehensive survey on virtually try-on outfits

Ghodhbani

Néji

Razzak

et al. 2022

Multimed Tools Appl

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Since the last years and until now, technology has made fast progress for many industries, in particularly, garment industry which aims to follow consumer desires and demands. One of these demands is to fit clothes before purchasing them on-line. Therefore, many research works have been focused on how to develop an intelligent apparel industry to ensure the online shopping experience. Image-based virtual try-on is among the most potential approach of virtual fitting that tries on target clothes into customer's image, therefore, it has received considerable research efforts in the recent years. However, there are several challenges involved in development of virtual try-on that make it difficult to achieve naturally looking virtual outfit such as shape, pose, occlusion, illumination cloth texture, logo and text etc. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of virtual try-on. This review first introduces virtual try-on and its challenges followed by its demand in fashion industry. We summarize state-of-the-art image based virtual try-on for both fashion detection and fashion synthesis as well as their respective advantages, drawbacks, and guidelines for selection of specific try-on model followed by its recent development and successful application. Finally, we conclude the paper with promising directions for future research.

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Section: Applicationmentioning

confidence: 99%

You can try without visiting: a comprehensive survey on virtually try-on outfits

Ghodhbani

Néji

Razzak

et al. 2022

Multimed Tools Appl

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“…aat ini masyarakat menggunakan sistem rekomendasi dalam kehidupan sehari-hari diantaranya untuk online shopping, membaca artikel, menonton film, atau memilih musik. Sistem rekomendasi produk dapat mempengaruhi preferensi konsumen, perilaku pembelian, user experiences, dan penjualan (Zhang & Bockstedt, 2020), (Wu & Ye, 2020), (Turkut, Tuncer, Savran &Yilmaz, 2020). Sistem rekomendasi biasanya menggunakan predicting ratings atau mengembangkan daftar ranking produk untuk tiap pengguna.…”

Section: Pendahuluanunclassified

Pemodelan Sistem Rekomendasi Cerdas Menggunakan Hybrid Deep Learning

Dewi¹,

Ciptayani²

2022

SISTEK

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Trend perkembangan teknologi saat ini adalah mengarah ke sistem cerdas. Namun saat ini belum ada yang menggabungkan dua metode deep learning pada algoritma rekomendasi, sehingga penting untuk melakukan pemodelan sistem rekomendasi cerdas menggunakan hybrid deep learning. Penelitian ini bertujuan untuk mendapatkan model hybrid deep learning yang optimal pada sistem rekomendasi cerdas. Penelitian ini menggunakan pendekatan penelitian eksperimen. Teknik pengumpulan data yang digunakan meliputi observasi, penelusuran online dan pencatatan dataset. Tahapan penelitian terdiri dari: (a) literature review, (b) observasi dan pencarian online, (c) modeling (d) prototyping dan (e) testing. Model yang dihasilkan merupakan model hybrid deep learning yang terdiri dari dua layer yaitu layer Self Organizing Map (SOM) dan layer Recurrent Neural Network (RNN). Penelitian ini menggunakan bahasa pemrograman Python pada tahap pembuatan prototipe. Beberapa modul library dalam python yang digunakan antara lain numpy, pandas, tensorflow, hard, torch, sklearn. Program diuji dengan dataset dari kaggle.com. Hasil pengujian berhasil meningkatkan kinerja dengan meningkatkan akurasi hingga 100%. Dapat disimpulkan bahwa model SOM-RNN dapat meningkatkan kinerja sistem rekomendasi cerdas.

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“…The results show that the image has a moderate influence in some cases and not in others with respect to the perceived value variable, only 37.3% know of any textile MiPyMe with an online store, this shows that, for Micro, small and medium companies In Colombia, Mexico and Peru, digital marketing strategies must be implemented to have a greater presence among online clothing consumers. In this regard, (71) found that the image of a store influences the perceived value, provided that the quality perceived by the customer and the trust in the store maintain high levels; however, this is difficult to achieve when you have volatile online consumers who spend a lot of time comparing features and prices in different virtual stores, looking for the perfect item to suit their needs (11)(12)(13)(14).…”

Section: Contrasting the Hypothesesmentioning

confidence: 99%

“…Online shopping is almost a pure visual experience, where consumers visit virtual stores, search for the items they want, compare features and prices, choose the perfect item and place their order through Internet services (11) (12). When shopping online, consumers need a long time to search for all compatible products, and they often shop at more than one of the retail brands; Therefore, brand advertising greatly influences the purchase decision in any of the virtual stores visited by customers (13) (14).…”

Section: Introductionmentioning

confidence: 99%

Determinants of the Intention to Buy Clothes in the Online Stores of the MiPyMe of Perú, Colombia and México

García-Salirrosas¹,

Henao²,

Gordillo³

et al. 2021

Preprint

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With the recent coronavirus pandemic (Covid-19) online sales of clothing retail have become a necessity to survive in the market. It is for this reason that the objective of the research was to know the purchase intention of clothing by young consumers through the online channel of small and micro companies (MiPyMe) located in the countries of Mexico, Colombia and Peru. To do this, using a structural equations model, it was intended to analyze the relationships between the variables trust, satisfaction, perceived value, quality of interaction and image in the electronic context as antecedent variables of online purchase intention, in accordance with the theoretical framework. 638 surveys were applied to young consumers in Mexico, Colombia and Peru. Sociodemographic characteristics included gender, age, country, and knowledge of a MiPyMe online clothing store. The results reveal that, of the total of young people surveyed, 62.7% stated that they did not know of any online clothing store of a MiPyMe. The contrasting of the hypotheses indicates that the quality of interaction directly and positively influences perceived value, that the image directly and positively influences the satisfaction and purchase intention of the consumer of the MiPyMe of Colombia, Mexico and Peru. These results may be useful for entrepreneurs in this business sector to rethink their online business strategies, as well as for the governments of these nations to promote this sector with training and specialized technical assistance.

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An Online Recommendation System Using Deep Learning for Textile Products

Cited by 12 publications

References 14 publications

You can try without visiting: a comprehensive survey on virtually try-on outfits

You can try without visiting: a comprehensive survey on virtually try-on outfits

Pemodelan Sistem Rekomendasi Cerdas Menggunakan Hybrid Deep Learning

Determinants of the Intention to Buy Clothes in the Online Stores of the MiPyMe of Perú, Colombia and México

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