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 large-scale 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 users' 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 users' 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 user's interest in the photos, it would be possible to extract the user's preferences from the sightseeing place photos. The feasibility of the proposed method is evaluated by several experiments using a prototype of our system.