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Hybrid followee recommendation in microblogging systems


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Followee recommendation plays an important role in information sharing over microblogging plat-forms. 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.


关注对象推荐在微博平台信息共享中发挥了重要的作用。现有推荐方法主要基于内容相关性或社交图谱信息对关注用户对象进行排序, 难以获得理想的性能。观察到微博系统同时是社交网络和社交媒体的双重属性, 提出一种新颖的用户关注对象推荐方法。该方法同时考虑了微博推文和社交结构两种信息源, 建立了一种线性加权模型将两种因素结合起来, 并设计了一种模拟退火算法用于自动对两种因素的权重组合进行优化求解。基于从新浪微博系统中采集的真实系统数据进行了全面的实验评估。结果证明本方法相对于现有方法提供了更为精确的微博关注对象推荐。

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Correspondence to Hanhua Chen.

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Chen, H., Jin, H. & Cui, X. Hybrid followee recommendation in microblogging systems. Sci. China Inf. Sci. 60, 012102 (2017). https://doi.org/10.1007/s11432-016-5551-7

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  • online social networks
  • microblogging
  • followee recommendation
  • simulated annealing
  • ranking


  • 在线社交网络
  • 微博
  • 关注对象推荐
  • 模拟退火
  • 排序