research-article

Informed Recommender: Basing Recommendations on Consumer Product Reviews

Authors:

Silvana Aciar,

Debbie Zhang,

Simeon Simoff,

John DebenhamAuthors Info & Claims

IEEE Intelligent Systems, Volume 22, Issue 3

Pages 39 - 47

https://doi.org/10.1109/MIS.2007.55

Published: 01 May 2007 Publication History

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Abstract

Consumer reviews, opinions, and shared experiences in using a product are a powerful source of information that recommender systems can use. Despite the importance and value of such information, no comprehensive mechanism formalizes the opinions' selection, retrieval, and use owing to the difficulty of extracting information from text data. A new recommender system prioritizes consumer product reviews on the basis of the reviewer's level of expertise in using a product. The system uses text mining techniques to map each piece of each review comment into an ontology. Using consumer reviews also helps solve the cold-start problem that plagues traditional approaches. This article is part of a special issue on Recommender Systems.

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Cited By

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Lei JZhu CYang SWang JYu Y(2023)Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating PredictionNeural Processing Letters10.1007/s11063-023-11363-555:8(11035-11054)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11363-5
Duan RJiang CJain H(2022)Combining review-based collaborative filtering and matrix factorizationDecision Support Systems10.1016/j.dss.2022.113748156:COnline publication date: 1-May-2022
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Mekouar LIraqi YDamaj INaous T(2022)A survey on blockchain-based Recommender SystemsComputer Communications10.1016/j.comcom.2022.01.020187:C(1-19)Online publication date: 1-Apr-2022
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Index Terms

Informed Recommender: Basing Recommendations on Consumer Product Reviews
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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Published In

IEEE Intelligent Systems Volume 22, Issue 3

May 2007

85 pages

ISSN:1541-1672

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IEEE Educational Activities Department

United States

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Published: 01 May 2007

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Lei JZhu CYang SWang JYu Y(2023)Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating PredictionNeural Processing Letters10.1007/s11063-023-11363-555:8(11035-11054)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11363-5
Duan RJiang CJain H(2022)Combining review-based collaborative filtering and matrix factorizationDecision Support Systems10.1016/j.dss.2022.113748156:COnline publication date: 1-May-2022
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Abstract

References

Cited By

Index Terms

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