With the rapid development of smart devices and wireless communication, especially with the pre-launch of Google Glass, augmented reality (AR) has received enormous at- tention recently. AR adds virtual objects into a user's real- world environment enabling live interaction in three dimen- sions. Limited by the small display of AR devices, content selection is one of the key issues to improve user experi- ence. In this paper, we present an aggregated random walk algorithm incorporating personal preferences, location infor- mation, and temporal information in a layered graph. By adaptively changing the graph edge weight and computing the rank score, the proposed AR recommender system pre- dicts users' preferences and provides the most relevant rec- ommendations with aggregated informatio.