A Comparative Study of Outfit Recommendation Methods with a Focus on Attention-based Fusion

Laenen, Katrien; Moens, Marie‐Francine

doi:10.1016/j.ipm.2020.102316

Cited by 23 publications

(7 citation statements)

References 2 publications

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“…These studies aim to enhance the annotation process's object identification, segmentation, and classification algorithms [34,35,39,40,. Another group of studies develops techniques for advising and helping customers by pairing products or examining the textures of garments [78][79][80][81]84,[121][122][123][124][125][126][127][128]. Additionally, some authors [32, are developing virtual reality technology to help customers with clothing fitting.…”

Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

“…In order to transition the traditional buying experience to e-commerce, NLP is mostly utilized for fashion advice and recommendations [34,78,79,124,126,[155][156][157][158][159][160][161][162][163][164][165][166][167][168]. Another significant area of study focuses on gathering data from consumers using sentiment analysis techniques, such as via reviews or social media [42,44,[169][170][171][172][173][174][175][176][177][178][179][180].…”

Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

“…Combining various approaches is a very active topic of AI research. Since many of the works included in this review use a combination of CV and NLP techniques to achieve the goal, it was simple to see this effect [34,55,94,95,102,107,116,126,156,157]. When information from many modalities is included in a research problem, such as text-image, video-audio, or another combination, it is referred to as multimodal research.…”

Section: Multimodal Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

Section: Multimodal Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

View full text Add to dashboard Cite

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“…The pharmacological ontology assigns drugs according to the patient's health conditions, and the food ontology is used to determine an appropriate patient's metabolic rate and caloric intake. The work of Laenen & Moens (2020) proposes a deep learning‐based recommender system for an outfit recommendation. This work compares different fusion methods for outfit recommendation, which combine product features extracted from visual and textual data in semantic, multimodal item representations.…”

Section: Related Workmentioning

confidence: 99%

A hybrid recommender system based on description/dialetheic logic and linked data

Santos

Aguilar

2022

Expert Systems

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In this research, we developed a hybrid recommender system based on description/dialetheic logic, which provides an innovative architecture for the recommendation based on ambiguous reasoning. In addition, our approach allows enriching the knowledge during the reasoning using the linked data paradigm. Thus, the architecture allows the integration of linked data, in order to be exploited by the recommender. On the other hand, the reasoning mechanisms of dialetheic logic allow handling situations of contradiction or inconsistency, to determine the information to offer to users. According to the reviewed literature, our proposal is the first that implements a dialetheic engine in a Recommender System. In general, there are a lot of works about the utilization of linked open data (LOD) to improve the Recommender Systems, for example, to enrich the information to recommend or to solve the cold start problem. However, there are no proposals to deal with the problems of ambiguity, incoherence or incompleteness of the information in these environments extended with LOD. In this way, this paper proposes a hybrid reasoning engine that uses the linked data paradigm for the knowledge extraction, and dialetheic logic for the management of inconsistency/ambiguity information, in order to obtain recommendations. Particularly, the information extracted with Linked Data is processed with the Dialetheic Logic reasoner to solve ambiguous cases. Thus, each recommendation is enriched with related content extracted from the linked data sources, which has been disambiguated. The results are very promising since our hybrid reasoning mechanism allows obtaining more precise recommendations, considering the different classical states of ambiguity in dialetheic logic (contingent statements about the future, failure of a presupposition, vagueness, counterfactual reasoning), according to various metrics of quality used to evaluate the recommendations achieved.

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“…Prior work on multimodal retrieval in e-commerce has been mainly focused on cross-modal retrieval for fashion [6,16,42]. Other related examples include outfit recommendation [15,19,21] Some prior work on interpretability for fashion product retrieval proposes to leverage multimodal signals to improve explainability of latent features [20,38]. Tautkute et al [31] propose a multimodal search engine for fashion items and furniture.…”

Section: Related Workmentioning

confidence: 99%

Extending CLIP for Category-to-image Retrieval in E-commerce

Hendriksen¹,

Bleeker²,

Vakulenko³

et al. 2021

Preprint

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E-commerce provides rich multimodal data that is barely leveraged in practice. One aspect of this data is a category tree that is being used in search and recommendation. However, in practice, during a user's session there is often a mismatch between a textual and a visual representation of a given category. Motivated by the problem, we introduce the task of category-to-image retrieval in e-commerce and propose a model for the task, CLIP-ITA. The model leverages information from multiple modalities (textual, visual, and attribute modality) to create product representations. We explore how adding information from multiple modalities (textual, visual, and attribute modality) impacts the model's performance. In particular, we observe that CLIP-ITA significantly outperforms a comparable model that leverages only the visual modality and a comparable model that leverages the visual and attribute modality.

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A Comparative Study of Outfit Recommendation Methods with a Focus on Attention-based Fusion

Cited by 23 publications

References 2 publications

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

A hybrid recommender system based on description/dialetheic logic and linked data

Extending CLIP for Category-to-image Retrieval in E-commerce

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