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SCIENCE CHINA Information Sciences, Volume 60, Issue 1: 012102(2017) https://doi.org/10.1007/s11432-016-5551-7

Hybrid followee recommendation in microblogging systems

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  • ReceivedOct 16, 2015
  • AcceptedDec 7, 2015
  • PublishedNov 23, 2016

Abstract

Followee recommendation plays an important role in information sharing over microblogging platforms. Existing followee recommendation schemes adopt either content relevance or social information for followee ranking, suffering poor performance. Based on the observation that microblogging systems have dual roles of social network and news media platform, we propose a novel followee recommendation scheme that takes into account the information sources of both tweet contents and the social structures. We set up a linear weighted model to combine the two factors and further design a simulated annealing algorithm to automatically assign the weights of both factors in order to achieve an optimized combination of them. We conduct comprehensive experiments on real-world datasets collected from Sina Weibo, the largest microblogging system in China. The results demonstrate that our scheme provides a much more accurate followee recommendation for a user compared to existing schemes.


Acknowledgment

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Grant Nos.~61370233, 61422202), Research Fund of Guangdong Province (Grant No. 2015B010131001), and Foundation for the Author of National Excellent Doctoral Dissertation of China (Grant No. 201345).


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