Abstract
We are currently witnessing a growing tendency for users to engage in social activities on the Internet. Consequently, social networks - tools that enable such activities - are increasingly used. This paper describes a system that uses information users share on these networks (personal context), to recommend Web feeds of related content to users. The system mines data from popular social networks and combines it with information from third party websites to create user profiles. Subsequently, these profiles are matched with appropriately tagged Web feeds and are displayed to users through a mobile device application. We evaluate the system with a 6-month test run involving real users. We measure the system’s performance as well as user satisfaction and assessment of the accuracy of recommendations by including a feedback mechanism on the mobile application.
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Karapantelakis, A., Maguire, G.Q. (2010). Utilizing Social Context for Providing Personalized Services to Mobile Users. In: Lukowicz, P., Kunze, K., Kortuem, G. (eds) Smart Sensing and Context. EuroSSC 2010. Lecture Notes in Computer Science, vol 6446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16982-3_3
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DOI: https://doi.org/10.1007/978-3-642-16982-3_3
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