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Ilyoung Choi
Graduate School of Business Administration, KyungHee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea

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Journal article
Published: 30 May 2021 in Sustainability
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Information technology and the popularity of mobile devices allow for various types of customer data, such as purchase history and behavior patterns, to be collected. As customer data accumulate, the demand for recommender systems that provide customized services to customers is growing. Global e-commerce companies offer recommender systems to gain a sustainable competitive advantage. Research on recommender systems has consistently suggested that customer satisfaction will be highest when the recommendation algorithm is accurate and recommends a diversity of items. However, few studies have investigated the impact of accuracy and diversity on customer satisfaction. In this research, we seek to identify the factors determining customer satisfaction when using the recommender system. To this end, we develop several recommender systems and measure their ability to deliver accurate and diverse recommendations and their ability to generate customer satisfaction with diverse data sets. The results show that accuracy and diversity positively affect customer satisfaction when applying a deep learning-based recommender system. By contrast, only accuracy positively affects customer satisfaction when applying traditional recommender systems. These results imply that developers or managers of recommender systems need to identify factors that further improve customer satisfaction with the recommender system and promote the sustainable development of e-commerce.

ACS Style

Jaekyeong Kim; Ilyoung Choi; Qinglong Li. Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches. Sustainability 2021, 13, 6165 .

AMA Style

Jaekyeong Kim, Ilyoung Choi, Qinglong Li. Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches. Sustainability. 2021; 13 (11):6165.

Chicago/Turabian Style

Jaekyeong Kim; Ilyoung Choi; Qinglong Li. 2021. "Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches." Sustainability 13, no. 11: 6165.

Journal article
Published: 29 January 2020 in Sustainability
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A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers’ repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers’ purchasing order and customers’ repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers’ purchasing orders and customers’ repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.

ACS Style

Hea In Lee; Il Young Choi; Hyun Sil Moon; Jae Kyeong Kim. A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks. Sustainability 2020, 12, 969 .

AMA Style

Hea In Lee, Il Young Choi, Hyun Sil Moon, Jae Kyeong Kim. A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks. Sustainability. 2020; 12 (3):969.

Chicago/Turabian Style

Hea In Lee; Il Young Choi; Hyun Sil Moon; Jae Kyeong Kim. 2020. "A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks." Sustainability 12, no. 3: 969.

Journal article
Published: 26 September 2012 in Information Technology and Management
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Korea, once a typical post-war underdeveloped country, has achieved significant economic growth, becoming one of the G20 economies. In particular, during the past decades, the information technology (IT) sector (including telecommunications, consumer electronics, and computer games) has occupied a great portion of Korea’s economy and the use of IT in non-IT sectors (such as manufacturing, logistics, banking, non-profit sectors, and retail services including e-commerce and m-commerce) has grown substantially [3]. Korea’s early economic success in 1970s and 1980s were mainly due to the development in heavy industries. In early 1990s, Korea experienced 80–10 % GDP growth rates. Then, the Asian financial crisis initiated in Thailand in the mid-1990s affected the Korean economy significantly. The unemployment rate reached up to 6.8 % in 1998 as several large Korean conglomerates including Hanbo, Sammi, Jinro, and Halla went bankrupt and financial sectors reported large loses. As a result of

ACS Style

Young U. Ryu; Jae Kyeong Kim; Il Young Choi. The role of IT in Korea’s economic development. Information Technology and Management 2012, 14, 3 -6.

AMA Style

Young U. Ryu, Jae Kyeong Kim, Il Young Choi. The role of IT in Korea’s economic development. Information Technology and Management. 2012; 14 (1):3-6.

Chicago/Turabian Style

Young U. Ryu; Jae Kyeong Kim; Il Young Choi. 2012. "The role of IT in Korea’s economic development." Information Technology and Management 14, no. 1: 3-6.