Abstract
In this paper, we propose Tracommender, a context-aware recommender system, which uses background tracking information from smartphones to generate location-based recommendations. Based on the automatically collected data that consist of locations with timestamps, the dwell time at certain locations can be derived in order to use it as an implicit rating for a location-based collaborative filtering. We further introduce two alternative path matching algorithms that utilize continuous location sequences (paths) to compute path patterns between similar users. In addition, in order to overcome the cold-start problem of recommender systems, clustering algorithms are used to calculate so-called Activity Zones - locations taken from an existing database of categorized points of interest. Synthesized movement data has been applied to perform evaluations on performance, scalability and precision of an implemented prototype of the proposed recommendation algorithms.
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Wang, Y., Uzun, A., Bareth, U., Küpper, A. (2013). Tracommender – Exploiting Continuous Background Tracking Information on Smartphones for Location-Based Recommendations. In: Borcea, C., Bellavista, P., Giannelli, C., Magedanz, T., Schreiner, F. (eds) Mobile Wireless Middleware, Operating Systems, and Applications. MOBILWARE 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 65. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36660-4_18
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DOI: https://doi.org/10.1007/978-3-642-36660-4_18
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