Home // ICDS 2022, The Sixteenth International Conference on Digital Society // View article

Personalized Item Review Ranking Method Based on Empathy

Authors:
Taketoshi Ushiama
Daichi Minami

Keywords: online reviews; recommendations; rankings; natural language processing; machine learning.

Abstract:
In e-commerce, online reviews posted about items play an essential role in helping users select products. However, when many reviews are posted for the same product, it is sometimes difficult for users to find the most valuable reviews among them. This paper proposes a method for ranking online reviews of a target item based on the user's empathy for reviewers. Using the target user's feedback on reviews for known items as input, the proposed method estimates the empathy toward the reviewer and ranks reviews for unknown items based on it. Evaluation experiments showed that the proposed method is effective against comparative methods.

Pages: 42 to 43

Copyright: Copyright (c) IARIA, 2022

Publication date: June 26, 2022

Published in: conference

ISSN: 2308-3956

ISBN: 978-1-61208-981-2

Location: Porto, Portugal

Dates: from June 26, 2022 to June 30, 2022