Abstract
The rapidly growing interest in healthy lifestyles and the health benefit of foods and the growing tea-consuming population are driving the growth of the tea industry. In particular, the growing preference among Millennials for premium blended tea is leading the growth of the tea market. In this paper, we study the feasibility of recommendation services for blended tea, which has not been addressed well by existing recommender systems. To this end, we design TeaPickTM, a mobile application that suggests a blend of tea suited to the user’s preferences including desired health benefits. To evaluate the application and its recommendations, we conduct not only a usability test, but also a consumer acceptance test with 31 participants. Our user study shows that the participants were positive about the recommendation service provided by our application and were generally satisfied with the recommended tea.
Highlights
The paradigm of decision-making in consumer behavior is gradually shifting from people to machines [1]
According to the report from Zion Market Research [5], the global tea market is forecasted to show significant growth; the market value was around USD 49,456.52 million in 2017 and it will grow at a compound annual growth rate (CAGR) of about 4.5% between 2018 and
We study the feasibility of recommendation services for blended teas, which has not been addressed well by existing recommendation services
Summary
The paradigm of decision-making in consumer behavior is gradually shifting from people to machines [1]. The advance of information communication technology (ICT), and machine learning and artificial intelligence (AI) technology have been driving this paradigm shift, causing many changes in the purchasing behavior of consumers [1,2]. Consumers conveniently order products at online shopping malls without having to check them directly at offline stores. They receive automatically generated recommendations on the products, films, music, and food that they want, and complete their purchases via recommendation algorithms, without the assistance of a sales person. Consumers are increasingly relying on ICT-based decision-making systems based on recommendation algorithms and big data analyses, such as recommender systems, for minimized shopping expenses and maximized convenience. According to the report from Zion Market Research [5], the global tea market is forecasted to show significant growth; the market value was around USD 49,456.52 million in 2017 and it will grow at a compound annual growth rate (CAGR) of about 4.5% between 2018 and
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