A Recommendation System of Sightseeing Places based on Users Behavior of Taking and Editing Photos With the advancement of digital camera functions that are installed in smartphones, the behavior of taking photos and sharing them with others has become a daily experience for many people. Meanwhile, for travelers, it is important to select sightseeing places and plan an appropriate route to them. There are many studies on recommendation systems for sightseeing places and their routes. However, in these conventional studies, there is a problem that the information of a place that is reluctantly visited by a traveler, such as on a business trip, becomes noise and, as a result, it might cause a recommendation system to suggest a sightseeing place that does not match the interests of the traveler. In addition, in general, it is difficult to form a good recommendation unless a largescale data set of sightseeing places is collected and each location is actively evaluated by many users. In this study, we propose a method for extracting usersxe2x80x99 preferences based on the behavior of taking and editing photos of sightseeing places. The feature of our method is that it calculates the degrees of interest in photos that are actually taken by a user by counting the numbers of pictures and editing processes such as color adjustments and clipping, which are regarded as a part of the usersxe2x80x99 behavior of taking and editing photos of sightseeing places. This is done for the purpose of providing recommendations of sightseeing places. By associating the result of the content analysis of the sightseeing place photos with the degrees of a userxe2x80x99s interest in the photos, it would be possible to extract the userxe2x80x99s preferences from the sightseeing place photos. The feasibility of the proposed method is evaluated by several experiments using a prototype of our system.

Moments of Interest: A novel cloudbased crowdsourcing application enhancing smart tourism recommendations In the modern era where the tourism industry continues to grow and contribute both socially and economically at global scale, a demanding need to boost the tourism sector through IT arises. This need along with recent, technology driven, behavior patterns where users, with the use of their smartphones capture a wide number of images daily has led to the proposal of a new concept namely xe2x80x9cMoments of Interestxe2x80x9d (MOIs). This novel concept is realized on an innovative mobile application that aims at offering personalized recommendations to tourists based on special xe2x80x9cmomentsxe2x80x9d residing in user captured images. In order to achieve this, image labeling through machine learning is utilized and a novel cloudbased xe2x80x9cMemories Databasexe2x80x9d is created.