From Recommendation to Generation: A Novel Fashion Clothing Advising Framework

Yang, Zilin; Su, Zhuo; Yang, Yang; Guo, Lin

doi:10.1109/icdh.2018.00040

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…Besides fashion items and outfit recommendations, a few similar tasks are gaining attention in the fashion and retail industry. One of these areas includes image-based fashion generation, where methods have been proposed to generate outfits so that the user could explore them and judge their compatibility [79,91,156]. One of the key techniques used in these works is Generative Adversarial Networks (GANs), which will be described in detail in Section 3.2.…”

Section: Trustmentioning

confidence: 99%

“…[Yang et al & 𝑐 + GAN] Yang et al [157] address the same problem settings of MrCGAN [126] by proposing a fashion clothing framework composed of two parts: a clothing recommendation model based on BPR combined with visual features and a clothing complementary item generation based GAN. Notably, the generation component takes as input a piece of clothing recommended in the recommendation model and generates clothing images of other categories (i.e., tops, bottom, or shoes) to build a set of complementary items.…”

Section: Generative Fashion Recommendation Modelsmentioning

confidence: 99%

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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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Section: Trustmentioning

confidence: 99%

Section: Generative Fashion Recommendation Modelsmentioning

confidence: 99%

A Review of Modern Fashion Recommender Systems

Deldjoo¹,

Nazary²,

Ramisa³

et al. 2022

Preprint

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“…VAE-GAN-CC [82] 2018 ✓ ✓ ✓ RecSys-DAN [121] 2019 ✓ ✓ ✓ FR-DiscoGAN [59] 2019 ✓ ✓ ✓ DASO [39] 2019 ✓ ✓ ✓ CnGAN [88] 2019 ✓ ✓ ✓ Fashion Rec. DVBPR [60] 2017 ✓ ✓ ✓ CRAFT [57] 2018 ✓ ✓ ✓ MrCGAN [105] 2018 ✓ ✓ ✓ Yang et al [135] 2018 ✓ ✓ ✓ c + GAN [64] 2019 ✓ ✓ ✓ vector-wise training in which G generates continuous-valued vectors to avoid misleading D, which in turn improves the performance of both G and D. The authors show the improvement of CFGAN over IRGAN and GraphGAN baselines. As an example, with regards to P@20 on the Ciao dataset, the improvement is 100% for CFGAN vs. IRGAN (0.45 v.s.…”

Section: Model Namementioning

confidence: 99%

“…The results show that MrCGAN can generate compatible and realistic images under compatibility learning setting compared to baselines. [135] address the same problem settings of MrCGAN [105] by proposing a fashion clothing framework composed of two parts: a clothing recommendation model based on BPR combined with visual features and a clothing complementary item generation based GAN. Notably, the generation component takes in input a piece of clothing recommended in the recommendation model and generates clothing images of other categories (i.e., top, bottom, or shoes) to build up a set of complementary items.…”

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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“…Social-networkbased recommendation [7,8,31,43,92,133,[138][139][140][141][142][143][144][145][146][147][148][149][150][151][152] • Personalized clothing recommendation built using three types of data:…”

mentioning

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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From Recommendation to Generation: A Novel Fashion Clothing Advising Framework

Cited by 11 publications

References 8 publications

A Review of Modern Fashion Recommender Systems

A Review of Modern Fashion Recommender Systems

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Fashion Recommendation Systems, Models and Methods: A Review

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