Abstract
Location recommendation aims at providing personalized suggestions of a set of new and potentially interesting locations to a target user. The underlying principle of this problem is to predict the Degree of Relevance of candidate locations to the user and make recommendations accordingly. Enormous attention has been devoted to this problem by research and industrial community lately due to its applicability in numerous applications. In this work we develop an effective GPS trajectory-based location recommendation framework for Location Based Social Networks. We propose an algorithm, STS Location Recommender, to leverage unique properties of GPS trajectories namely spatial, temporal and semantic features for recommendation. Our algorithm specifically exploits temporal and semantic influence on users’ mobility fused with spatial properties of locations to model relevance of locations to users. Prior to our work, no existing studies based on GPS trajectories simultaneously used all of these features for location recommendation. We experiment on real-world GPS datasets to show that our approach provides more precise recommendations compared with baseline approaches.
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Abdel-Fatao, H., Li, J., Liu, J. (2015). Unifying Spatial, Temporal and Semantic Features for an Effective GPS Trajectory-Based Location Recommendation. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_4
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DOI: https://doi.org/10.1007/978-3-319-19548-3_4
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