Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction

Version 1 : Received: 31 December 2019 / Approved: 2 January 2020 / Online: 2 January 2020 (05:34:39 CET)

How to cite: Li, Q.; Choi, I.; Kim, J. Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction. Preprints 2020, 2020010015 (doi: 10.20944/preprints202001.0015.v1). Li, Q.; Choi, I.; Kim, J. Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction. Preprints 2020, 2020010015 (doi: 10.20944/preprints202001.0015.v1).

Abstract

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

Subject Areas

expectancy disconfirmation theory; customer satisfaction; e-commerce personalized service; recommendation system; deep neural network

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