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Manually selecting subsets of photos from large collections in order to present them to friends or colleagues or to print them as photo books can be a tedious task. Today, fully automatic approaches are at hand for supporting users. They make use of pixel information extracted from the images, analyze contextual information such as capture time and focal aperture, or use both to determine a proper subset of photos. However, these approaches miss the most important factor in the photo selection process: the user. The goal of our approach is to consider individual interests. By recording and analyzing gaze information from the user's viewing photo collections, we obtain information on user's interests and use this information in the creation of personal photo selections. In a controlled experiment with 33 participants, we show that the selections can be significantly improved over a baseline approach by up to 22% when taking individual viewing behavior into account. We also obtained significantly better results for photos taken at an event participants were involved in compared with photos from another event.