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Recommending messages to users in social networks: a cross-site study

R. Cohen, N. Sardana, K. Rahim, D. Y. Lam, M. Li, O. Maccarthy, E. Woo, J. Zhang, G. Guo - Personal Name;

In this paper we produce an algorithm for pre- senting messages to users in social networks that integrates reasoning about the message, the author, the recipient and the social network. Our proposed model was derived on the basis of immersion within three different existing social networking environments, that of Coursera, Reddit, and medical self-help groups such as PatientsLikeMe. We first present three models, each of which is designed to perform well within the context of one specific social network. From here we derive a generalized model which can be effective regardless of social network context. We conclude with a discussion of possible directions for future research, with an emphasis on promoting the use of trust modeling and user modeling, in a view to exploring additional networks and include as well a comparison to competing models within the artificial intelligence literature. Our aim is to offer insights into coping with the massive amount of information that currently resides within our social networks.


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: ., 2010
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1-6
Language
English
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Recommending messages to users in social networks:
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Accra Metropolitan University is a forward-thinking, private higher education institution in Ghana dedicated to empowering minds and shaping futures for sustainable global development. Fully accredited by the Ghana Tertiary Education Commission (GTEC), the university is built on the core pillars of LIFE: Leadership, Innovation, Flexibility, and Entrepreneurship.

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