PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability

Sagar, Dikshant; Garg, Jatin; Kansal, Prarthana; Bhalla, Sejal; Shah, Rajiv Ratn; Yu, Yi

doi:10.1109/bigmm50055.2020.00039

Cited by 22 publications

(16 citation statements)

References 43 publications

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“…The MANN+MF and GP-BPR obtain mean average precision of 0.15 and 0.13, respectively, while retrieving the same number of items (~20 items). Additionally, Sagar et al introduced PAI-BPR (Personalized Attributewise Interpretable-BPR) as an outfit compatibility model that can capture user-item interaction along with general item-item interaction based on the user's personal preferences and identifying the discordant and harmonious attributes between fashion items [191]. They used multilayer perceptron (MLP) to learn the non-linear interactions and leverage both the textual and visual modalities in the context of item description and image, respectively.…”

Section: Collaborative Filtering (Cf) 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: Collaborative Filtering (Cf) Techniquementioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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“…49,153,52,2,121,101,180,69,122,90,79,183,146,133,73,139,76,64,16,3,90,119,141,91,156,138,10,179,44,189]. It can be seen in Figure10that the keywords of this group are Fashion, Clothing, System, Image, Attributes, Retrieval, Recommendation, and Classification.…”

mentioning

confidence: 95%

fAshIon after fashion: A Report of AI in Fashion

Zou,

Wong

2021

Preprint

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Sum ma ry dev elop me nts of tech nol ogy Ev al ua tio n pr ot oc ol s fo r sp ec ifi c ta sk s V is u a ll y u n d e rs ta n d in g o f fa sh io n im a g e s Key Points Detection Clothing Parsing Clothing Detection On lin e sel lin g Fashion Image Retrieval Attributes Recognition found ation for P e rs o n a l S t y li n g generated images category generated outfits fou nd ati on for color Ana lyze the tren ds and gett ing insi ght s for selli ng

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“…The task of compatible garment recommendation, which we address in this paper, is closely related to the one of outit compatibility estimation. This has been previously addressed in literature by several works [26,27,29,33]. The two settings difer since compatibility estimation establishes if two or more given items it well together, whereas, compatible garment recommendation proposes a ranked list of candidates that are compatible with a given item.…”

Section: Introductionmentioning

confidence: 99%

Disentangling Features for Fashion Recommendation

Divitiis

Becattini

Baecchi

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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Online stores have become fundamental for the fashion industry, revolving around recommendation systems to suggest appropriate items to customers. Such recommendations often suffer from a lack of diversity and propose items that are similar to previous purchases of an user. Recently, a novel kind of approach based on Memory Augmented Neural Networks (MANN) has been proposed, aimed at recommending a variety of garments to create an outfit by complementing a given fashion item. In this paper we address the task of compatible garment recommendation developing a MANN architecture by taking into account the co-occurrence of clothing attributes, such as shape and color, to compose an outfit. To this end we obtain disentangled representations of fashion items and store them in external memory modules, used to guide recommendations at inference time. We show that our disentangled representations are able to achieve significantly better performance compared to the state of the art and also provide interpretable latent spaces, giving a qualitative explanation of the recommendations.

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PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability

Cited by 22 publications

References 43 publications

Fashion Recommendation Systems, Models and Methods: A Review

Fashion Recommendation Systems, Models and Methods: A Review

fAshIon after fashion: A Report of AI in Fashion

Disentangling Features for Fashion Recommendation

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