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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.
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 StyleJaekyeong 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 StyleJaekyeong 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.
전 세계적으로 전자상거래 시장의 규모가 급속하게 커지면서 개인화 추천 서비스 관련 연구가 꾸준히 이루어지고 있다. 기존 개인화 추천 서비스 연구에서는 주로 고객의 명시적 평점 (Explicit Rating)과 암묵적 데이터 (Implicit Feedback)를 활용하여 고객의 선호도를 예측했다. 하지만 정량적인 데이터만 사용하면 추천의 정확도가 떨어진다. 본 연구에서는 고객 평점만을 고려하는 기존 추천 방법론의 한계를 극복하기 위해 고객의 정성적 선호도를 나타내는 댓글 데이터를 사용하여 고객에게 맞춤형 상품을 추천하는 방법을 제안하고자 한다. 본 연구에서는 CRSE 모델을 구축하고, 이를 통해 상품 구매할 때 고객에게 중요한 정보를 제공하는 댓글 데이터를 정교하게 분석하여 정량적인 선호도 점수를 산출하여 새로운 고객 프로파일을 구축하였다. 실험 결과, 본 연구에서 제안한 방법은 기존 추천 기법들보다 예측 정확도가 더 우수한 것으로 나타났다.
Jinzhe Yan; Jiaen Li; Qinglong Li. A Study on the Personalization Recommendation Service Incorporating Review-based Customer Profile. Journal of Digital Contents Society 2020, 21, 1575 -1584.
AMA StyleJinzhe Yan, Jiaen Li, Qinglong Li. A Study on the Personalization Recommendation Service Incorporating Review-based Customer Profile. Journal of Digital Contents Society. 2020; 21 (9):1575-1584.
Chicago/Turabian StyleJinzhe Yan; Jiaen Li; Qinglong Li. 2020. "A Study on the Personalization Recommendation Service Incorporating Review-based Customer Profile." Journal of Digital Contents Society 21, no. 9: 1575-1584.