Fashion Recommendation Systems, Models and Methods: A Review

Chakraborty, Samit; Hoque, Md. Saiful; Jeem, Naimur Rahman; Biswas, Manik Chandra; Bardhan, Deepayan; Lobatón, Edgar

doi:10.3390/informatics8030049

Cited by 42 publications

(17 citation statements)

References 194 publications

(259 reference statements)

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“…4.66 billion people access the internet, an increase of The associate editor coordinating the review of this manuscript and approving it for publication was Arianna Dulizia . 7.3% compared to January 2020. World internet penetration stands at 59.5%, but the values could be even higher by virtue of problems related to the correct tracking of internet users related to the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 93%

“…An example aims to study the impact and the significance of AI in the fashion industry in the last decades throughout the supply chain [5], while the most recent [6] has the aim to study the impact and significance of AI in fashion e-commerce. In the context of fashion recommendation system an interesting and recent review is presented by [7]. The authors in detail describe the technical aspects, strengths and weaknesses of the filtering techniques.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

et al. 2022

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Fashion knowledge encourages people to properly dress and faces not only physiological necessity of users, but also the requirement of social practices and activities. It usually includes three jointly related aspects of: occasion, person and clothing. Nowadays, social media platforms allow users to interact with each other online to share opinions and information. The use of social media sites such as Instagram has already spread to almost every fashion brand and been evaluated as business take-off tools. With the heightened use of social media as a means of marketing communication for fashion brands, it has become necessary to empirically analyse and extract fashion knowledge from them. Thus, social brands are investing on them. In this way, they can understand the consumer's preferences. This change is also having a significant impact on social media data analysis. To solve this issue, the Deep learning (DL) methods are proven to be effective solutions due to their automatic learning capability. However, little systematic work currently exists on how researchers have applied DL for analysing fashion knowledge from social media data. Hence, this contribution outlines DL-based techniques for social media data related to fashion domain. In this study, a review of the dataset within the fashion world and the DL methods applied on, it is presented to help out new researchers interested in this subject. In particular, five different tasks will be considered: Object Detection, that includes Clothes Landmark Detection, Clothes Parsing and Product Retrieval, Fashion Classification, Clothes Generation, Automatic Fashion Knowledge Extraction and Clothes Recommendation. Therefore, the purpose of this paper is to underline the multiple applications within the fashion world using deep learning techniques. However, this review does not cover all the methods used: in fact, only Deep Learning methods have been analyzed. This choice was made since, given the huge amount of fashion social media data that has been collected, Deep Learning methods achieve the best performance both in terms of accuracy and time. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

et al. 2022

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“…While collaborative filtering techniques only exploit past user purchases and product interactions to generate new recommendations [7], content-based methods rely on information about the users or the items [3]. Recommendation systems for fashion retail e-commerce, the domain of our work, frequently fall into the content-based category, and are often paired with images and textual descriptions of the products [1,4,5,8,9,21,25]. Our work is similar in being content-based to other works in fashion retail e-commerce, as it leverages textual user requests as the primary representation of the user.…”

Section: Related Workmentioning

confidence: 99%

Contrastive Learning for Interactive Recommendation in Fashion

Sevegnani¹,

Seshadri²,

Beniwal³

et al. 2022

Preprint

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Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths of recommender systems to accurately capture user tastes with search systems' ability to process user queries. We propose a novel remedy to this problem by automatically recommending personalized fashion items based on a user-provided text request. Our proposed model, WhisperLite, uses contrastive learning to capture user intent from natural language text and improves the recommendation quality of fashion products. WhisperLite combines the strength of CLIP embeddings with additional neural network layers for personalization, and is trained using a composite loss function based on binary cross entropy and contrastive loss. The model demonstrates a significant improvement in offline recommendation retrieval metrics when tested on a real-world dataset collected from an online retail fashion store, as well as widely used open-source datasets in different e-commerce domains, such as restaurants, movies and TV shows, clothing and shoe reviews. We additionally conduct a user study that captures user judgements on the relevance of the model's recommended items, confirming the relevancy of WhisperLite's recommendations in an online setting.

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“…The main objective of recommender systems is to recommend relevant content tailored to the customer's preferences and intent. Fashion e-commerce has heavily invested in developing recommender systems for different use cases to aid online shopping experience [4,8] including recommending relevant items [2,9,35], complete outfits [3,10,15], and size recommendation [14,23].…”

Section: Related Workmentioning

confidence: 99%

Reusable Self-Attention Recommender Systems in Fashion Industry Applications

Celikik¹,

Ramallo²,

Wasilewski³

2022

Proceedings of the 16th ACM Conference on Recommender Systems

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A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, we leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions. We present both offline and online experimental results demonstrating substantial improvements in customer retention and engagement.

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Fashion Recommendation Systems, Models and Methods: A Review

Cited by 42 publications

References 194 publications

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

Contrastive Learning for Interactive Recommendation in Fashion

Reusable Self-Attention Recommender Systems in Fashion Industry Applications

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