Deep Fashion Recommendation System with Style Feature Decomposition

Shin, Yong-Goo; Yeo, Yoon-Jae; Sagong, Min-Cheol; Ji, Seo-Won; Ko, Sung-Jea

doi:10.1109/icce-berlin47944.2019.8966228

Cited by 16 publications

(10 citation statements)

References 14 publications

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“…Therefore, most of the model-based CF algorithms categorize the user into multiple clusters [184]. With the evolution of the use of learning algorithms, model-based recommendation systems have begun to use some algorithms such as association rules, clustering, decision tree, artificial neural network, link analysis, regression and Bayesian classifiers [57,193,194].…”

Section: Model-based Collaborative Filtering Techniquementioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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Section: Model-based Collaborative Filtering Techniquementioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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“…In addition to serving as a reference, image data can also help track fashion trends [179], identify user preferences [180,181], product matching recommendations [182,183,184,185], and prevent infringement [158]. For instance, Hu et al [186] established a furniture visual classification model containing 16 styles (e.g., Gothic style, Modernist style, and Rococo style), which combined image features extracted by CNN with handcrafted features.…”

Section: Product Design Based On Image Datamentioning

confidence: 99%

Big Data driven Product Design: A Survey

Quan¹,

Li²,

Zeng³

et al. 2021

Preprint

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With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.Keywords product design • big data • Kansei engineering • QFD • generative design * Correspondence.

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“…Sun et al [30,31] try to model the category tree to get different item representations in different hierarchical spaces. Recently, some researches on fashion recommendation begin to adopt more attribute tags [2,11,34] and content information [12,29,33] to enrich items. Different from previous works, our proposed model directly represents items by their attributes.…”

Section: Item Representation Learningmentioning

confidence: 99%

Disentangled Item Representation for Recommender Systems

Liu

et al. 2020

Preprint

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Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price and style of clothing). Utilizing these attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this paper, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications.CCS Concepts: • Applied computing → Online shopping.

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Deep Fashion Recommendation System with Style Feature Decomposition

Cited by 16 publications

References 14 publications

Fashion Recommendation Systems, Models and Methods: A Review

Fashion Recommendation Systems, Models and Methods: A Review

Big Data driven Product Design: A Survey

Disentangled Item Representation for Recommender Systems

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