Enhancing Fashion Recommendation with Visual Compatibility Relationship

Yin, Ruiping; Li, Kan; Lu, Jie; Zhang, Guangquan

doi:10.1145/3308558.3313739

Cited by 44 publications

(16 citation statements)

References 17 publications

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“…Lee et al [37] proposed Style2Vec to learn and encode the items' visual style into vector representations for the downstream fashion recommendation, i.e., to recommend an outfit of items with good compatibility. Similarly, Yin et al [7] and Li et al [38] learn and utilize visual compatibility relationship between items to enhance fashion recommendation or complementary item recommendation. Hidayati et al [8] try to match clothing style and personal body shape for effective fashion recommendation.…”

Section: Item Visual Appearance Learning In Recommendationmentioning

confidence: 99%

“…where W d r , W d z and σ s are the analogical weight matrices and activation functions in Eqs. (6) (7).…”

Section: B Preference Learning Modulementioning

confidence: 99%

“…In practice, in a sequence of user-item interactions, a user's choice on items not only depends on her main preference over items' function, but also depends on her auxiliary visual preference [6,7]. In this paper, a user's visual preference refers to her/his preference regarding the appearances and styles of items and it can be revealed by the appearance images of those items chosen by the user, such as the appearance and style of a clothes [8].…”

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confidence: 99%

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Learning Users’ Visual Preferences for Improving Recommendations

Wang

Yang

et al. 2022

IEEE Access

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Sequential recommender systems (SRSs) aim to predict the next item interest to a user by learning the users' dynamic preferences over items from the sequential user-item interactions. Most of existing SRSs make recommendations by only modeling a user's main preference towards the functions of items, while ignoring the user's auxiliary visual preference towards the appearances and styles of items. Although visual preference is less significant than the main preference, it may still play an important role in most of users' choice on items. On the one hand, a user often prefers to choose the item which matches her/his visual preference well from multiple items with the same function. For example, a lady may choose one clothes whose style suits her best from multiple clothes with the same function. On the other hand, some particular users (e.g., young girls) are usually very concerned about the appearances of some special items (e.g., clothes, jewelry). Therefore, the overlook of modeling users' visual preference may generate unsatisfied recommendations which can not match a user's various types of preferences and thus reduce the consumption experience. To address this gap, in this paper, we propose modeling users' visual preferences to improve the performance of sequential recommendations. Specifically, we devise a coupled Double-chain Preference learning Network (DPN) to jointly learn a user's main preference and visual preference as well as the interactions between them. In DPN, one chain is for modeling a user's main preference by taking the IDs of items as the input and the other chain is for modeling the user's visual preference by taking appearance images of items as the input. Finally, the two types of preferences are carefully integrated with an attention module for the next item prediction. Extensive experiments on two real-world transaction datasets show the superiority of our proposed DPN over the representative and state-of-the-art SRSs. INDEX TERMS visual preference, recommendations, deep neural networks I. INTRODUCTION

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Section: Item Visual Appearance Learning In Recommendationmentioning

confidence: 99%

“…where W d r , W d z and σ s are the analogical weight matrices and activation functions in Eqs. (6) (7).…”

Section: B Preference Learning Modulementioning

confidence: 99%

mentioning

confidence: 99%

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Learning Users’ Visual Preferences for Improving Recommendations

Wang

Yang

et al. 2022

IEEE Access

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“…Fashion recommendation systems (FRSs) generally provide specific recommendations to the consumer based on their browsing and previous purchase history. Social-network-based FRSs consider the user's social circle, fashion product attributes, image parsing, fashion trends, and consistency in fashion styles as important factors since they impact upon the user's purchasing decisions [28][29][30][31][32][33][34][35][36][37][38]. FRSs have the ability to reduce transaction costs for consumers and increase revenue for retailers.…”

Section: Introductionmentioning

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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“…Therefore, personalisation is essential because it ensures outfit recommendation is in line with the users personal fashion taste and incorporates users likes and dislikes from a variety of perspectives. Personalised fashion recommendation has gained a lot of attention in recent work [6]- [9]. However, most of them fail to address the cold-start problem [10] for new users (See Figure 1) and solving this issue forms the basis for our approach.…”

Section: Introductionmentioning

confidence: 99%

Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling

Verma

Gulati

Shah

2020

2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)

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With the global transformation of the fashion industry and a rise in the demand for fashion items worldwide, the need for an effectual fashion recommendation has never been more. Despite various cutting-edge solutions proposed in the past for personalising fashion recommendation, the technology is still limited by its poor performance on new entities, i.e. the cold-start problem. In this paper, we attempt to address the cold-start problem for new users, by leveraging a novel visual preference modelling approach on a small set of input images. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation. Quantitatively, our results show that the proposed visual preference modelling approach outperforms state of the art in terms of clothing attribute prediction. Qualitatively, through a pilot study, we demonstrate the efficacy of our system to provide diverse and personalised recommendations in cold-start scenarios.

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Enhancing Fashion Recommendation with Visual Compatibility Relationship

Cited by 44 publications

References 17 publications

Learning Users’ Visual Preferences for Improving Recommendations

Learning Users’ Visual Preferences for Improving Recommendations

Fashion Recommendation Systems, Models and Methods: A Review

Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling

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