Real-time and personalized product recommendations for large e-commerce platforms
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911024605560832 |
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| author | Tolloso, Matteo Bacciu, Davide Mokarizadeh, Shahab Varesi, Marco |
| author_facet | Tolloso, Matteo Bacciu, Davide Mokarizadeh, Shahab Varesi, Marco |
| contents | We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one of the largest e-commerce platforms demonstrates the effectiveness of our approach in forecasting purchase sequences and handling multi-interaction scenarios, achieving efficient personalized recommendations under real-world constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time and personalized product recommendations for large e-commerce platforms Tolloso, Matteo Bacciu, Davide Mokarizadeh, Shahab Varesi, Marco Information Retrieval Artificial Intelligence We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one of the largest e-commerce platforms demonstrates the effectiveness of our approach in forecasting purchase sequences and handling multi-interaction scenarios, achieving efficient personalized recommendations under real-world constraints. |
| title | Real-time and personalized product recommendations for large e-commerce platforms |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2506.21368 |