APJIS Asia Pacific Journal of Information Systems

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The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus Indexed Journal,
is published by the Korea Society of Management Information Systems (KMIS),
which is the largest professional institute in the field of information systems in Korea.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Hee-Woong Kim

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Current Issue

Date June 2019
Vol. No. Vol. 29 No. 2
DOI https://doi.org/10.14329/apjis.2019.29.2.203
Page 203~216
Title Assessing Personalized Recommendation Services Using Expectancy Disconfirmation Theory
Author Il Young Choi, Hyun Sil Moon, Jae Kyeong Kim
Keyword Expectancy Disconfirmation Theory, Customer Satisfaction, Disconfirmation, Accuracy, Diversity, Personalized Recommendation Service
Abstract There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diver-sity dilemma with personalized recommendation services. To this end, we develop a personalized recom-mendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recom-mendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recom-mendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers’ preferences cor-rectly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the num-ber of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.


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