By allowing people to manipulate digital content placed in the real world, Augmented Reality (AR) provides immersive and enriched experiences in a variety of domains. Despite its increasing popularity, providing a seamless AR experience under bandwidth fluctuations is still a challenge, since delivering these experiences at photorealistic quality with minimal latency requires high bandwidth. Streaming approaches have already been proposed to solve this problem, but they require accurate prediction of the Field-Of-View of the user to only stream those regions of scene that are most likely to be watched by the user. To solve this prediction problem, we study in this paper the watching behavior of users exploring different types of AR scenes via mobile devices. To this end, we introduce the ACE Dataset, the first dataset collecting movement data of 50 users exploring 5 different AR scenes. We also propose a four-feature taxonomy for AR scene design, which allows categorizing different types of AR scenes in a methodical way, and supporting further research in this domain. Motivated by the ACE dataset analysis results, we develop a novel user visual attention prediction algorithm that jointly utilizes information of users' historical movements and digital objects positions in the AR scene. The evaluation on the ACE Dataset show the proposed approach outperforms baseline approaches under prediction horizons of variable lengths, and can therefore be beneficial to the AR ecosystem in terms of bandwidth reduction and improved quality of users' experience.