Home // International Journal On Advances in Networks and Services, volume 16, numbers 1 and 2, 2023 // View article

Review Ranking to Support Selection of Recommended Items

Authors:
Taketoshi Ushiama
Daichi Minami

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

Abstract:
This paper proposes a novel approach to aid product selection in e-commerce through the effective ranking of online reviews. Often, users find it challenging to identify the most valuable information amidst a sea of reviews. Our approach addresses this by ranking reviews based on the user's empathy towards reviewers. By taking user feedback on reviews of known products, we estimate the level of empathy towards the reviewer, subsequently ranking reviews of unknown items accordingly. This enables users to easily pinpoint the most relevant reviews amidst the multitude of information. Our evaluation experiments have revealed this new approach to be superior to traditional comparative methods.

Pages: 36 to 42

Copyright: Copyright (c) to authors, 2023. Used with permission.

Publication date: June 30, 2023

Published in: journal

ISSN: 1942-2644