PREFER is an online preference learning system that generates personalized review summaries and improves alignment with user interests in simulations on Amazon review data.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
W-RAC decouples extraction from semantic planning via structured units and LLM grouping to match traditional retrieval performance at roughly 10x lower LLM token cost.
citing papers explorer
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PREFER: Personalized Review Summarization with Online Preference Learning
PREFER is an online preference learning system that generates personalized review summaries and improves alignment with user interests in simulations on Amazon review data.
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Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systems
W-RAC decouples extraction from semantic planning via structured units and LLM grouping to match traditional retrieval performance at roughly 10x lower LLM token cost.