Personalized fashion recommender system with image based neural networks

Sridevi, M.; ManikyaArun, N.; Sheshikala, M.; Sudarshan, E. C. G.

doi:10.1088/1757-899x/981/2/022073

Cited by 40 publications

(5 citation statements)

References 8 publications

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“…FastkNN [152] FCNN [56] FNN [32] Fuzzylogic [106,113] GAN [71,75,99,129,134,141,155,251] GA-RF [65] GaussianMixtureModels [109,118] gradientboostingdecisiontree [42] GraphCNN [40,122] GraphDCNN [89] GRU [230] GSN [87] H-CNN [39,157] HOG [153] HypergraphNN [80] Imageprocessing [119,120,130,132,135,136,147] k-center [117] k-means [114,203,232,244] k-medoids [115] Kneser-Ney [34] kNN [54,137,222,235,237,250] LAC [107,116] Lassoregression …”

Section: Methods Citationsmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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Section: Methods Citationsmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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“…First, they used a CNN (AlexNet) and the batch-normalized inception (BN inception) classifier to extract features, and then submitted them to the k-NN ranking algorithm and returned the top-N matching style recommendations. Sridevi et al [15] proposed novel fashion recommendations for the user based on the input given. First, they extracted the features of the image using the ResNet50 classifier, and then uploaded fashion images from a website and generated similar images based on the input image's features and texture.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Image recommendation deals with finding the most similar objects for a given object, where the object is present as an image. A fashion recommendation system involves matching features between fashion products and consumers; it can be defined as a set of criteria used to match fashion products with users or consumers [15,16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fashion Classification with Vision Transformer (ViT) and Developing Recommendation Fashion Systems Using DINOVA2

Abd Alaziz,

Elmannai,

Saleh

et al. 2023

Electronics

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As e-commerce platforms grow, consumers increasingly purchase clothes online; however, they often need clarification on clothing choices. Consumers and stores interact through the clothing recommendation system. A recommendation system can help customers to find clothing that they are interested in and can improve turnover. This work has two main goals: enhancing fashion classification and developing a fashion recommendation system. The main objective of fashion classification is to apply a Vision Transformer (ViT) to enhance performance. ViT is a set of transformer blocks; each transformer block consists of two layers: a multi-head self-attention layer and a multilayer perceptron (MLP) layer. The hyperparameters of ViT are configured based on the fashion images dataset. CNN models have different layers, including multi-convolutional layers, multi-max pooling layers, multi-dropout layers, multi-fully connected layers, and batch normalization layers. Furthermore, ViT is compared with different models, i.e., deep CNN models, VGG16, DenseNet-121, Mobilenet, and ResNet50, using different evaluation methods and two fashion image datasets. The ViT model performs the best on the Fashion-MNIST dataset (accuracy = 95.25, precision = 95.20, recall = 95.25, F1-score = 95.20). ViT records the highest performance compared to other models in the fashion product dataset (accuracy = 98.53, precision = 98.42, recall = 98.53, F1-score = 98.46). A recommendation fashion system is developed using Learning Robust Visual Features without Supervision (DINOv2) and a nearest neighbor search that is built in the FAISS library to obtain the top five similarity results for specific images.

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“…In Table 3, we provide a classification of data/computational features used for the design of fashion RS by authors over the years. ✓ ✓ ✓ ✓ Hu et al [65] ✓ ✓ Cardoso et al [21] ✓ ✓ ✓ Jagadeesh et al [71] ✓ ✓ ✓ Liu et al [94] ✓ ✓ Zhang et al [163] ✓ ✓ Qian et al [115] ✓ ✓ ✓ ✓ ✓ ✓ Zhao et al [164] ✓ ✓ ✓ McAuley et al [107] ✓ ✓ Pan et al [111] ✓ Sun et al [131] ✓ ✓ ✓ Tangseng and Okatani [133] ✓ ✓ ✓ Sridevi et al [130] ✓ Brand and Gross [20] ✓ ✓ Xing et al [ [120]. Matrix factorization (MF), or BPR-MF, is one of the most promising approaches to building CF.…”

Section: Categorization Based On Inputmentioning

confidence: 99%

A Review of Modern Fashion Recommender Systems

Deldjoo¹,

Nazary²,

Ramisa³

et al. 2022

Preprint

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The textile and apparel industries have grown tremendously over the last years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion RS can have a noticeable impact on billions of customers' shopping experiences and increase sales and revenues on the provider-side.The goal of this survey is to provide a review of recommender systems that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, explainability, among others) and type of side-information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics. CCS Concepts: • Information systems → Recommender systems; Multimedia and multimodal retrieval; • Human-centered computing → User models.

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Personalized fashion recommender system with image based neural networks

Cited by 40 publications

References 8 publications

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Enhancing Fashion Classification with Vision Transformer (ViT) and Developing Recommendation Fashion Systems Using DINOVA2

A Review of Modern Fashion Recommender Systems

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