Abstract
It is important to discover informative users disseminating fresh and high-quality domain-specific contents over social media in order to keep up-to-date with and learn cutting-edge knowledge, but that is not easy, especially for new learners due to information abundance or even overload. We propose an efficient approach to discover potential informative users by matching real-world authors extracted from the latest domain-specific publications to corresponding social media user accounts. Mutually reinforcing methods are further applied to identify informative users and recommend domain-specific contents in social media. Our experiments on real data from arxiv and twitter are used to verify feasibility and effectiveness of the proposed methods.
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Wang, J., Xiang, J., Uchino, K. (2016). Domain-Specific Recommendation by Matching Real Authors to Social Media Users. In: Chiu, D., Marenzi, I., Nanni, U., Spaniol, M., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2016. ICWL 2016. Lecture Notes in Computer Science(), vol 10013. Springer, Cham. https://doi.org/10.1007/978-3-319-47440-3_27
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DOI: https://doi.org/10.1007/978-3-319-47440-3_27
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