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Supplementing Product Reviews: Retrieving Opinions from Products with Similar Attributes

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Marino Fujii
Takehiro Yamamoto
Takayuki Yumoto
Published in
Information Integration and Web Intelligence
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the challenge of limited product reviews for new or less popular items and proposes a solution by leveraging opinions from similar products. The study introduces a method that uses a Large Language Model (LLM) to identify relevant attributes and retrieve opinions from similar products, enhancing user decision-making. The research evaluates the effectiveness of this approach through both automatic and human evaluations, demonstrating that the proposed method outperforms baseline methods. Key topics include the transformation of text queries into attribute queries, the retrieval of similar products based on attribute similarity, and the extraction and ranking of opinions from product reviews. The study concludes that using opinions from similar products can significantly improve the quality of information available to users, making it a valuable tool for e-commerce platforms and retailers.
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previous chapter Survival Informatics: Reliable Social Media Analysis for Societal Well-Being
next chapter Generating Comparative Table by LLM-Based Product Review Summarization
Title
Supplementing Product Reviews: Retrieving Opinions from Products with Similar Attributes
Authors
Marino Fujii
Takehiro Yamamoto
Takayuki Yumoto
Copyright Year
2026
Book
Information Integration and Web Intelligence

Print ISBN: 978-3-032-11975-9

Electronic ISBN: 978-3-032-11976-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-11976-6_2
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