PREFER: Personalized Review Summarization with Online Preference Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 45 references that are in the map. First posted on arXiv 7 May 2026.
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References · 9
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
- 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
- 2021Reinforcement learning based recommender systems: A surveycited by 188
- 2014A* Samplingcited by 151
- 2017Multiple Instance Learning Networks for Fine-Grained Sentiment Analysiscited by 23
- 2013Linear Bandits in High Dimension and Recommendation Systemscited by 22
- 2017Perishability of Data: Dynamic Pricing under Varying-Coefficient Modelscited by 19
- 2020Fast Distributed Bandits for Online Recommendation Systemscited by 18
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