An Approach for Clothing Recommendation Based on Multiple Image Attributes

Sha, D. Q.; Wang, Daling; Zhou, Xiangmin; Feng, Shi; Zhang, Yifei; Yu, Ge

doi:10.1007/978-3-319-39937-9_21

Cited by 21 publications

(11 citation statements)

References 24 publications

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“…Most conventional RS are not suitable for application in the fashion domain due to unique characteristics hidden in this domain. For instance, people do not follow the crowd blindly when buying clothes or do not buy a fashion item twice [100]. Another aspect is related to the notion of complementary relationship for recommending a personalized fashion outfit.…”

Section: Fashion Recommendationmentioning

confidence: 99%

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Deldjoo

Noia

Merra

2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. Notwithstanding their great success, in recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs. The main reason for this behavior is that user interaction data used for training of LFM can be contaminated by malicious activities or users' misoperation that can induce an unpredictable amount of natural noise and harm recommendation outcomes. On the other side, it has been shown that these systems, conceived originally to attack machine learning applications, can be successfully adopted to strengthen their robustness against attacks as well as to train more precise recommendation engines.In this respect, the goal of this survey is two-fold: (i) to present recent advances on AML-RS for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs), which use the core concept of learning in AML (i.e., the min-max game) for generative applications.In this survey, we provide an exhaustive literature review of 60 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS and recommendation models leveraging generative models to improve their quality.

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Section: Fashion Recommendationmentioning

confidence: 99%

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Deldjoo

Noia

Merra

2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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“…[8,15,20,31] used CNN features of product images while [47] recommended movies with color histograms of posters and frames. [21,38,40] recommended clothes by considering the clothing fashion style.…”

Section: Image-based Recommendationsmentioning

confidence: 99%

“…There are many works recommending clothing or garments with fashion information [21,38,40] and there are several datasets for clothing fashion style. [21] utilized three datasets containing street fashion images and annotations by fashionistas to train phase, input queries, and return ranked list respectively.…”

Section: Rationality Of Using the Ava Dataset (Rq3)mentioning

confidence: 99%

Aesthetic-based Clothing Recommendation

Zhang

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

188

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Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the aesthetic features, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

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“…In addition to the above work, Iwata et al (2011) [10] offered a recommender system, utilizing fashion magazines' full-body photographs. In the same way, Sha et al (2016) [11] extracted multiple features from images to analyze their contents in different attributes, such as fabric pattern, collar, and sleeve. Some garment system integrates the fashion themes and shapes professional designers' knowledge and perception to help them choose the most relevant garment design scheme for a specific customer [12].…”

Section: Related Workmentioning

confidence: 99%

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

Zhang

Liu

Shi

et al. 2017

Scientific Programming

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With the rapid rising of living standard, people gradually developed higher shopping enthusiasm and increasing demand for garment. Nowadays, an increasing number of people pursue fashion. However, facing too many types of garment, consumers need to try them on repeatedly, which is somewhat time-and energy-consuming. Besides, it is difficult for merchants to master the real-time demand of consumers. Herein, there is not enough cohesiveness between consumer information and merchants. Thus, a novel fashion evaluation method on the basis of the appearance weak feature is proposed in this paper. First of all, image database is established and three aspects of appearance weak feature are put forward to characterize the fashion level. Furthermore, the appearance weak features are extracted according to the characters' facial feature localization method. Last but not least, consumers' fashion level can be classified through support vector product, and the classification is verified with the hierarchical analysis method. The experimental results show that consumers' fashion level can be accurately described based on the indexes of appearance weak feature and the approach has higher application value for the clothing recommendation system.

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An Approach for Clothing Recommendation Based on Multiple Image Attributes

Cited by 21 publications

References 24 publications

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Aesthetic-based Clothing Recommendation

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

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