Abstract
This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.
Highlights
The development and spread of the Internet and smart devices have contributed significantly to an increase in the traffic of web, app, and socialService network services (SNS) platforms
Table 7manner summarizes the research related to the recommendation system used in the tourism service field among papers related collectedtoaccording to the research criteriaused of this 7 summarizes thethe research the recommendation system in the study. service field among the papers collected according to the research criteria of tourism this study
In this chapter, using the large amount of research data that was collected by keylimiting the journal ranking in Google Scholar from 2010–2021, the market size, or the value words related to the application service field using the recommendation system, and withof major services for each application field by collecting various data about the system, we out limiting the journal ranking in Google Scholar from 2010–2021, the market size, or the derive a comprehensive trend in the development of recommendation systems
Summary
The development and spread of the Internet and smart devices have contributed significantly to an increase in the traffic of web, app, and SNS platforms. Recommendation systems already have a history of more than 29 years, the reason why it has been receiving particular attention recently is the video content streaming field, exemplified by Netflix, which is rapidly expanding its market This service field recommends video content reflecting the user’s taste by analyzing a vast amount of image content, information data, user activity data, and user data similar to that of the user. Unlike the previous section, which included data from top-tier journals, the research for this section was conducted to provide insight based on the entire data field in order to analyze the flow of overall research and service fields Through these two research approaches, we intend to increase the reliability of the major recommendation systemrelated contents and provide a stepping stone for setting the direction for future research by grasping the flow of a large amount of research. Several platform services actively recommend personalized items that meet the needs of users by introducing a recommendation
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