Open Access
Graph-Based Learning And Compressed Visual Features For Enhanced Fashion Recommendation
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Centre for Visual Computing, Indian Institute of Technology Delhi, India
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Institute for Artificial Intelligence, Sorbonne University, Paris, France
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Department of Computer Science, Politecnico di Milano, Italy
Abstract
Fashion recommendation systems play a crucial role in enhancing user experience and driving sales in the rapidly growing e-commerce landscape. Effective recommendation in the fashion domain necessitates capturing nuanced user preferences and item characteristics, particularly visual attributes. Traditional recommendation methods often struggle with the high dimensionality of visual data and the complex relationships between users and items. This article explores a novel approach for efficient and accurate visual-aware fashion recommendation by integrating graph-based learning with compressed visual features. We propose a conceptual framework where visual features extracted from fashion items are compressed to reduce dimensionality, and a graph is constructed to model user-item interactions and item-item relationships. Graph Neural Networks (GNNs) are then employed to learn rich embeddings on this graph, incorporating the compressed visual information. This approach is hypothesized to improve recommendation accuracy by jointly leveraging structural relationship data and visual content while enhancing efficiency and scalability by managing the high dimensionality of visual features. We discuss the key components of this framework, potential benefits, and challenges, highlighting its potential to advance the state of the art in fashion recommendation systems.
How to Cite
Dr. Rajeev K. Menon, Prof. Sophie Dubois, & Isabella Conti. (2024). Graph-Based Learning And Compressed Visual Features For Enhanced Fashion Recommendation. Frontiers in Emerging Artificial Intelligence and Machine Learning, 1(1), 13–20. Retrieved from https://irjernet.com/index.php/feaiml/article/view/26
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