Impact on recommendation performance of online review helpfulness and consistency
| DOI | https://doi.org/10.1108/DTA-04-2022-0172 |
| Published date | 21 April 2023 |
| Date | 21 April 2023 |
| Pages | 199-221 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Jaeseung Park,Xinzhe Li,Qinglong Li,Jaekyeong Kim |
Impact on recommendation
performance of online review
helpfulness and consistency
Jaeseung Park
Department of Business Administration, Kyung Hee University, Seoul, Seoul,
Republic of Korea
Xinzhe Li and Qinglong Li
Department of Big Data Analytics, Kyung Hee University, Seoul, Republic of
Korea, and
Jaekyeong Kim
School of Management, Kyung Hee University, Seoul, Republic of Korea and
Department of Big Data Analytics, Kyung Hee University, Seoul, Republic of Korea
Abstract
Purpose –The existingcollaborative filtering algorithmmay select an insufficiently representative customer
as the neighborof a target customer, which means that theperformance in providing recommendations is not
sufficiently accurate. This study aims to investigate the impact onrecommendation performance of selecting
influential andrepresentative 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 Online review, Text mining, Review helpfulness, Recommender system, Recommendation
performance, Review consistency
Paper type Research paper
1. Introduction
The online e-commerce market is growing explosively with recent developments in
information and communication technology and the popularization of smartphones.
Accordingly, the size of online shopping transactions is growing steadily. Thus, new
items and services are released regularly and accessibility and convenience for customers
have improved. However, there is an information overload problem in that the cost of
information search increases for customers making purchase decisions. In other words,
Funding: This research was supported by the Industrial Technology Innovation Program (20009050) and
the Ministry of Trade, Industry & Energy (MOTIE, Korea).
ThecurrentissueandfulltextarchiveofthisjournalisavailableonEmeraldInsightat:
https://www.emerald.com/insight/2514-9288.htm
199
Received 25 April 2022
Revised 10 July 2022
Accepted 17 August 2022
Data Technologies and
Applications
Vol. 57 No. 2, 2023
pp. 199-221
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-04-2022-0172
Impact of online
review on
recommender
system
selectingan item suited to customer preferencefrom among many items takesa long time and
is challenging (Park et al.,2012;Su and Khoshgoftaar, 2009;Kh odabandehlou et al.,2020).
Recently, the demandfor online shopping has soared; however, customers face limitations in
checking and experiencing their preferred items or services, which highlights the problem of
informationoverload. Furthermore,many companies have difficulty generating p rofitsdue to
reduced opportunities to promote anddisplay their items or services to customerswho prefer
them and are likelyto purchase them. Accordingly, a personalized recommendation service is
essential for providing personalized items or services to customers. For example, global
e-commerce companies such as Amazon, Netflix and Google provide personalized
recommendation services to strengthentheir sustainable corporate competitiveness (Bennett
and Lanning, 2007;Das et al., 2007;Lindenet al., 2003). Amazon generates 35 per cent of its
corporate salesthrough items or services that areprovided by personalized recommendation
services. Netflix delivers 75 per cent of all videos that are viewed by customers through
personalized recommendation services. As such, personalized recommendation services can
reduce the cost of searching for information and positively impact corporate revenue
generation (Lee and Hosanagar, 2019).
The collaborative filtering (CF) algorithm is the most widely used of many recommender
systems (Kim et al., 2012,2010b;Khodabandehlou et al., 2020). CF algorithms are implemented
based on the following assumption: customers with similar preferences for certain items exhibit
similar preferences for other items. Based on this assumption, the CF algorithm predicts
preferences based on the similarity between customers. The CF algorithm measures the
similarity between the target customer and other customers to select a customer with high
similarity as a neighbor to the target customer, and it predicts the preference of the target
customer according to the neighbor’s preference. The core idea of the CF algorithm is to select
a customer group that indicates preferences similar to those of the target customer. Here, similar
customers are usually referred to as nearest neighbors (Ricci et al.,2011). Nevertheless, existing
CF algorithms may select less representative customers as neighbors of their target customers.
This means that the recommendation performance is not accurate enough when providing
recommendations. With the development of the Internet and smart devices, unstructured data
related to customers and transactions are continuously increasing. Such a rapid increase in data
helps to improve the performance of the recommender system, but on the other hand, it also
decreases the performance of the recommender system due to increased noise (Kim et al.,2012).
Therefore, in order to reduce computing cost and provide effective recommendation service,
a strategy to improve the performance of the recommendation algorithm by filtering only
influential and meaningful data is required along with research to develop a new
recommendation algorithm to increase the recommendation performance (Dong-Hui and
Guang 2013). However, there have been few studies on how changes in input data affect
recommendation system performance in the recommendation system research so far.
Therefore, it is essential to investigate the impact of selecting influential and representative
customers on recommendation performance. Recently, some studies have utilized review-related
information as an additional feature to provide personalized recommendation services; online
reviews contain specific and reliable information that effectively provides recommendations
(Liu et al.,2013;Li et al.,2021). Many previous studies have argued that such online reviews
influence customers’purchase decision-making processes (Ham et al.,2019;Hlee et al., 2019;
Mitra and Jenamani, 2021). Aghakhani et al. (2021) argue that when consumers process online
review information, they simultaneously process review texts and their attendant star ratings.
In other words, consistency between a review text and its attendant star rating affects
information decision-making. Another study argues that helpful reviews have an essential
influence on purchase decision-making (Cheung et al., 2009;Hlee et al., 2019). Based on
previous studies, the authors selected neighbor customers based on review consistency and
helpfulness to address the problem of insufficient representative neighbor customers. Review
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