Abstract
In this paper, we propose a framework to improve the relevance of awareness information about people and subjects, by adapting recommendation techniques to real-time web data, in order to reduce information overload. The novelty of our approach relies on the use of contextual information about people’s current activities to rank social updates which they are following on Social Networking Services and other collaborative software. The two hypothesis that we are supporting in this paper are: (i) a social update shared by person X is relevant to another person Y if the current context of Y is similar to X’s context at time of sharing; and (ii) in a web-browsing session, a reliable current context of a user can be processed using metadata of web documents accessed by the user. We discuss the validity of these hypothesis by analyzing their results on experimental data.
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Joly, A., Maret, P., Daigremont, J. (2010). Contextual Recommendation of Social Updates, a Tag-Based Framework. In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_45
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DOI: https://doi.org/10.1007/978-3-642-15470-6_45
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