Impact on recommendation performance of online review helpfulness and consistency
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 8 September 2022
Issue publication date: 25 April 2023
Abstract
Purpose
The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.
Design/methodology/approach
Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.
Findings
This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.
Originality/value
This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.
Keywords
Acknowledgements
Funding: This research was supported by the Industrial Technology Innovation Program (20009050) and the Ministry of Trade, Industry & Energy (MOTIE, Korea).
Citation
Park, J., Li, X., Li, Q. and Kim, J. (2023), "Impact on recommendation performance of online review helpfulness and consistency", Data Technologies and Applications, Vol. 57 No. 2, pp. 199-221. https://doi.org/10.1108/DTA-04-2022-0172
Publisher
:Emerald Publishing Limited
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