PREFER: Personalized Review Summarization with Online Preference Learning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866915987579731968 |
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| author | Roy, Millend Capponi, Agostino Goyal, Vineet |
| author_facet | Roy, Millend Capponi, Agostino Goyal, Vineet |
| contents | Product reviews significantly influence purchasing decisions on e-commerce platforms. However, the sheer volume of reviews can overwhelm users, obscuring the information most relevant to their specific needs. Current e-commerce summarization systems typically produce generic, static summaries that fail to account for the fact that (i) different users care about different product characteristics, and (ii) these preferences may evolve with interactions. To address the challenge of unknown latent preferences, we propose an online learning framework that generates personalized summaries for each user. Our system iteratively refines its understanding of user preferences by incorporating feedback directly from the generated summaries over time. We provide a case study using the Amazon Reviews'23 dataset, showing in controlled simulations that online preference learning improves alignment with target user interests while maintaining summary quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05911 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PREFER: Personalized Review Summarization with Online Preference Learning Roy, Millend Capponi, Agostino Goyal, Vineet Artificial Intelligence Computer Science and Game Theory Machine Learning Systems and Control Optimization and Control Product reviews significantly influence purchasing decisions on e-commerce platforms. However, the sheer volume of reviews can overwhelm users, obscuring the information most relevant to their specific needs. Current e-commerce summarization systems typically produce generic, static summaries that fail to account for the fact that (i) different users care about different product characteristics, and (ii) these preferences may evolve with interactions. To address the challenge of unknown latent preferences, we propose an online learning framework that generates personalized summaries for each user. Our system iteratively refines its understanding of user preferences by incorporating feedback directly from the generated summaries over time. We provide a case study using the Amazon Reviews'23 dataset, showing in controlled simulations that online preference learning improves alignment with target user interests while maintaining summary quality. |
| title | PREFER: Personalized Review Summarization with Online Preference Learning |
| topic | Artificial Intelligence Computer Science and Game Theory Machine Learning Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2605.05911 |