This research examined whether the (in)congruence between the geographical distance between the viewer and the destination, and the dynamic distance experienced via zoomin and zoom-out affects the recommendation likelihood of the travel destination. Specifically, when the viewer’s motivation is utilitarian (e.g., travelling for work), we expect the congruence effect (H1): a higher recommendation likelihood when the geographic distance is congruent with the dynamic distance; that is, the viewer is more likely to recommend the travel destination when the destination is geographically far away from (close to) with a zoom-out (zoom-in) view. By contrast, when the viewer’s motivation is hedonic (e.g., travelling for fun), we expect the incongruence effect (H2): a higher recommendation likelihood when the geographic distance is incongruent with the dynamic distance; that is, the viewer is more likely to recommend the travel destination when the destination is geographically far away from (close to) with a zoom-in (zoomout) view. We test these ideas in an experimental study.

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.

오늘날 인터넷의 출현과 확산으로 인하여 정보의 홍수를 이루게 되었고, 고객들은 자신이 원하는 제품이나 서비스를 선택하기 위해서 정보를 탐색하는 작업이 더욱 어려워지게 되었다. 이러한 고객들에게 좀 더 편리하게 자신이 원하는 제품이나 서비스를 선택하도록 도와주는 것이 추천 시스템으로써, 고객 관계 관리의 중요한 부분으로 자리잡게 되었다. 본 연구에서는, 인터넷상의 여행사 사이트 등에서 고객이 여행지를 선택할 때 고객이 관심을 가질만한 여행지를 추천하여 줌