Understanding and Personalising Clothing Recommendation for Women

Costa, Evandro de Barros; Rocha, Hemilis Joyse Barbosa; Silva, Emanuele Tuane; Lima, Natalia Caroline; Cavalcanti, Juliana Veiga

doi:10.1007/978-3-319-56535-4_82

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…In determining the number of subjects, we referred to previous articles of the same type. [40][41][42][43] Furthermore, in conjunction with this article, although the steps of a systematic evaluation are simple, it requires a high degree of cooperation of the subjects and carefully judging the category of each set of collocation (satisfactory or relevant). In conclusion, this study faced 20 randomly selected testers from the major groups.…”

Section: Practical Application and Evaluationmentioning

confidence: 99%

Construction of womenswear matching expert system based on domain knowledge

Guo

et al. 2022

Textile Research Journal

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Clothing matching refers to the coordination of style, color, etc. to achieve a decent and generous effect. With the development of artificial intelligence, increasing research efforts have been dedicated to complementary garment collocation as matching clothes to make a suitable outfit has become a daily headache for many people. However, existing studies neglect rules regarding clothing matching, which are based on the knowledge accumulated in the fashion domain and are implicitly transmitted and ambiguous. Towards this end, this article proposes an expert system based on domain knowledge implemented using the Prolog program to realize rule-guided clothing collocation. The article constructs clothing matching rules from four essential clothing attributes: season, type, style, and color, and uses Prolog syntax for knowledge representation. For the formulated facts and rules, the reasoning machine iteratively matches the user's instructions with the system knowledge to reason out suitable matching suggestions. In this study we built a website as a man-machine interface to facilitate friendly interaction and set season, type, style, and color sub-options to match clothing and satisfy user preferences. The system validation based on standard metrics (precision, recall, F1-measure) achieves results above 81%. The main contribution is that the system could match clothes more accurately according to the likes and requirements of users. After comparing with other research, the system is expected to provide suggestions or references for people to choose style and dressing.

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Section: Practical Application and Evaluationmentioning

confidence: 99%

Construction of womenswear matching expert system based on domain knowledge

Guo

et al. 2022

Textile Research Journal

View full text Add to dashboard Cite

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“…This algorithm introduces a user-item linked list, which can reduce the space complexity of the algorithm. De Barros Costa et al [18] introduced an approach that recommends items based on fashion and users' body type. Packer et al [19] proposed an approach that learns users' visual preferences and predicts based on this.…”

Section: Related Workmentioning

confidence: 99%

ClothNet: A Neural Network Based Recommender System

Xing

Han

Shen

2020

Frontiers in Artificial Intelligence and Applications

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The traditional collaborative filtering recommendation systems have many deficiencies, which make them incompetent in the domain of clothing recommendation; we proposed a new ClothNet model based on CNN, RNN, collaborative filtering and the characteristics of the fashion industry. The accuracy and generalization performance of this model are improved compared with traditional systems. The visual information integrated into the ClothNet model enables the recommendation system to alleviate the cold start problem, and new clothes can be added to the recommendation list faster through the visual information. The addition of temporal information enables ClothNet sharply capturing the impact of seasonal and time changes on user preferences. However, because RNN and CNN have the disadvantage of requiring a large amount of data, combining RNN and CNN will make the model more difficult to converge, so we have adopted the LearningToRank training mode and obtained good results.

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“…Knowledge-based apparel recommendation system is an advanced recommendation system that learns clothes knowledge rather than tracking customer data. In research, based on a comprehensive review conducted by the authors (Guan et al , 2016) and latest updates, it is found that some recent studies have started to focusing on the integration of clothing and fashion knowledge, such as the fashion DNA, styling and body dressing in latest studies (Landia, 2017; Bracher et al , 2016; Perkinian and Vikkraman, 2015; Vaccaro et al , 2016; Vuruskan et al , 2015; de barros costa et al , 2017). The technologies required by such a system are from two aspects, namely, automatic apparel feature extractions and personalised clothes style recommendation.…”

Section: Related Workmentioning

confidence: 99%

Apparel-based deep learning system design for apparel style recommendation

Guan

Qin

Long

2019

IJCST

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The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people and know what to learn. This paper aims to explore an advanced apparel style learning and recommendation system that can recognise deep designassociated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert. Design/methodology/approach This study first proposes a new clothes style training data. Secondly, it designs three intelligent apparel learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models' performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a Convolutional Neural Network joint with the baseline classifier Support Vector Machine and the other is with a newly proposed classifier Later Kernel Fusion. Findings The results show that the most accurate model (with average prediction rate of 88.1%) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that (1) adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5%, Model B to 86%, Model C), and (2) the new concept of apparel recommendation based on style meanings is technically applicable. Originality/value The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

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Understanding and Personalising Clothing Recommendation for Women

Cited by 7 publications

References 5 publications

Construction of womenswear matching expert system based on domain knowledge

Construction of womenswear matching expert system based on domain knowledge

ClothNet: A Neural Network Based Recommender System

Apparel-based deep learning system design for apparel style recommendation

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