The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A spatial model of interaction in large virtual environments
ECSCW'93 Proceedings of the third conference on European Conference on Computer-Supported Cooperative Work
Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System
IEEE Intelligent Systems
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolution of a location-based online social network: analysis and models
Proceedings of the 2012 ACM conference on Internet measurement conference
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With the rapid development of smart devices and wireless communication, especially with the pre-launch of Google Glass, augmented reality (AR) has received enormous attention recently. AR adds virtual objects into a user's real-world environment enabling live interaction in three dimensions. Limited by the small display of AR devices, content selection is one of the key issues to improve user experience. In this paper, we present an aggregated random walk algorithm incorporating personal preferences, location information, and temporal information in a layered graph. By adaptively changing the graph edge weight and computing the rank score, the proposed AR recommender system predicts users' preferences and provides the most relevant recommendations with aggregated information.