Abstract
This paper describes recommendation techniques that help users to find potentially interesting people to follow at Twitter. The explored techniques are based on a confirmed assumption that the recent activity of users is indicative of their latest friend preferences. Several content-based recommendation strategies are explored, compared and tested. Among them the foundations for a novel hybridization framework are provided and a multi-view approach towards modeling user profiles is considered. The training and test database is crawled with real users and tweets from the Twitter network. A non-standard evaluation scheme is applied in an offline testing context for the various algorithms. Conclusions are drawn as to the viability, relative predictive power and accuracy of the recommendation approaches.
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Chechev, M., Georgiev, P. (2012). A Multi-view Content-Based User Recommendation Scheme for Following Users in Twitter. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds) Social Informatics. SocInfo 2012. Lecture Notes in Computer Science, vol 7710. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35386-4_32
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DOI: https://doi.org/10.1007/978-3-642-35386-4_32
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