The context-based review recommendation system in e-business platform

Ki-Kwang Lee, Hong-Hee Lee, Su-Ji Cho and Gyung-Su Min ()
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Ki-Kwang Lee: Dankook University
Hong-Hee Lee: St. Mary’s University
Su-Ji Cho: Dankook University
Gyung-Su Min: Dankook University

Service Business, 2022, vol. 16, issue 4, No 10, 1013 pages

Abstract: Abstract With the phenomenal growth of e-commerce, online review systems have become the normative dissemination mode of electronic word-of-mouth (eWOM). Unlike traditional WOM, consumers experience information overload in eWOM, thus they often read only a few reviews before making their purchase decision. Consumers tend to search for the most helpful and useful reviews from the large volume of posted reviews. To identify the most relevant reviews, this study applied both non-context features that affect the helpfulness of reviews and the context information that the review texts imply. The test performance and the results of the proposed method more effectively extracted reviews that provided the helpful information to consumers than the ordinary voting-based top-review list.

Keywords: Online review systems; Review helpfulness; Machine learning; Imbalanced environment (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11628-022-00502-y

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