Helping Online Fashion Customers Help Themselves: Personalised Recommender Systems

Pereira, Artur; Costa, Evandro de Barros; Vieira, Thales; Landim, André R.D.B.; Moura, José Fábio Paulino de

doi:10.1007/978-3-031-11185-3_2

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The integration of AI into the fashion domain represents a profound technological revolution, reshaping the very fabric of how the industry operates and innovates (Saponaro et al, 2018). This transformative era is characterized by the convergence of AI with traditional fashion processes, resulting in breakthrough advances and novel applications (Mohammadi & Kalhor, 2021). From the use of machine learning algorithms for predictive analytics to the application of deep learning techniques for sophisticated image recognition, AI is not just a tool but a catalyst that is redefining the boundaries of fashion design, production, marketing, and consumption (Chen et al, 2023).…”

Section: A Technical Perspective On Artificial Intelligence In the Fa...mentioning

confidence: 99%

“…How does AI affect the fashion workforce, and what are the wider socio-economic implications? An example of these considerations is the issue of lookism, discrimination based on a person's aesthetic characteristics, including aspects of appearance and ethnicity (Minerva, 2016). The use of AI in fashion raises critical questions about the perpetuation or mitigation of such biases.…”

Section: Introductionmentioning

confidence: 99%

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Ethical Implications of Artificial Intelligence in the Fashion Industry

Giovanola,

Tiribelli,

Frontoni

et al. 2024

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In fashion domain, companies increasingly navigate a complex web of data involving intricate correlations, dependencies, and the unpredictability of human behavior. Managing these diverse data flows is critical to improving decision-making in an industry that depends on both creativity and precision. In this context, artificial intelligence (AI) techniques have emerged as powerful tools that offer unparalleled efficiency in interpreting and using these huge datasets. However, as the industry moves deeper and deeper into this digital frontier, it is encountering a wide range of ethical concerns. This paper examines this intersection, exploring both the technological breakthroughs that AI is bringing to fashion and the ethical implications that accompany this digital evolution. We discuss the need for robust frameworks and guidelines to ensure the responsible use of AI, noting its potential to both increase and mitigate the fashion industry’s environmental impact.

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Section: A Technical Perspective On Artificial Intelligence In the Fa...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ethical Implications of Artificial Intelligence in the Fashion Industry

Giovanola,

Tiribelli,

Frontoni

et al. 2024

View full text Add to dashboard Cite

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“…Recommendation systems can be helpful for people who are looking for new fashion ideas and inspiration, or for those who are looking for specific items but are not sure where to start [1]. It is always a good idea to carefully consider the recommendations of a fashion recommendation system before making a purchase.…”

Section: Introductionmentioning

confidence: 99%

Smart Fashion Recommendation System using FashionNet

Challa,

Sathwik,

Kiran

et al. 2023

ICST Transactions on Scalable Information Systems

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An intelligent system known as a fashion suggestion system gives consumers personalised fashion advice based on their tastes, style, body shape, and other variables. The system analyses a user's data and predicts the best fashion products for them using data analytics, machine learning, and artificial intelligence approaches. Intelligent fashion suggestion is currently desperately needed due to the explosive expansion of fashion-focused trends. We create algorithms that automatically recommend users' attire based on their own fashion tastes. We investigate the use of deep networks to this difficult problem. Our technology, called FashionNet, is made up of two parts: a matching network for determining compatibility and a feature network for feature extraction. We create a two-stage training method that transfers a broad compatibility model to a model that embeds personal choice in order to achieve personalised recommendation.

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Music recommendation algorithms based on knowledge graph and multi-task feature learning

Liu,

Yang,

Cheng

2024

Sci Rep

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During music recommendation scenarios, sparsity and cold start problems are inevitable. Auxiliary information has been utilized in music recommendation algorithms to provide users with more accurate music recommendation results. This study proposes an end-to-end framework, MMSS_MKR, that uses a knowledge graph as a source of auxiliary information to serve the information obtained from it to the recommendation module. The framework exploits Cross & Compression Units to bridge the knowledge graph embedding task with recommendation task modules. We can obtain more realistic triple information and exclude false triple information as much as possible, because our model obtains triple information through the music knowledge graph, and the information obtained through the recommendation module is used to determine the truth of the triple information; thus, the knowledge graph embedding task is used to perform the recommendation task. In the recommendation module, multiple predictions are adopted to predict the recommendation accuracy. In the knowledge graph embedding module, multiple calculations are used to calculate the score. Finally, the loss function of the model is improved to help us to obtain more useful information for music recommendations. The MMSS_MKR model achieved significant improvements in music recommendations compared with many existing recommendation models.

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Helping Online Fashion Customers Help Themselves: Personalised Recommender Systems

Cited by 4 publications

References 16 publications

Ethical Implications of Artificial Intelligence in the Fashion Industry

Ethical Implications of Artificial Intelligence in the Fashion Industry

Smart Fashion Recommendation System using FashionNet

Music recommendation algorithms based on knowledge graph and multi-task feature learning

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