Abstract
In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM’s approach in terms of effectiveness as well as efficiency.
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Notes
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Second Screen Society: http://www.2ndscreensociety.com/.
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References
Socialising Around Media (SAM) Project: Dynamic Social and Media Content Syndication for 2nd Screen. http://samproject.net/
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Acknowledgment
This work has been supported by the SAM project and funded from the European Union’s 7th Framework Programme for research, technological development and demonstration under grant agreement no 611312.
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Valsamis, A., Psychas, A., Aisopos, F., Menychtas, A., Varvarigou, T. (2017). Second Screen User Profiling and Multi-level Smart Recommendations in the Context of Social TVs. In: Wu, TT., Gennari, R., Huang, YM., Xie, H., Cao, Y. (eds) Emerging Technologies for Education. SETE 2016. Lecture Notes in Computer Science(), vol 10108. Springer, Cham. https://doi.org/10.1007/978-3-319-52836-6_55
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