利用Twitter网络的主题功能推荐关注者

Brahim Dib, Fahd Kalloubi, E. Nfaoui, Abdelhak Boulaalam
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引用次数: 2

摘要

随着Twitter网络的快速发展,用户被通过追随者/追随者社交网络分享的海量信息所淹没,为了克服这一问题,寻找志同道合的用户成为一项非常重要的任务。因此,建议使用一个系统来协助用户完成这样的任务。在本文中,我们提出了一个追随者推荐系统,利用主题特征进行主题建模,并利用追随者/追随者拓扑,根据主题相似度搜索相似的用户进行推荐。为了展示我们方法的有效性,我们使用从Twitter平台收集的数据集来评估它。实验结果表明,我们的模型优于基于词汇的[reference?]方法和基于语义的方法[参考文献?],推荐10个关注者的召回值超过23%,证明处理微博网站内容中用户感兴趣的话题比处理语义和词汇特征更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging topic feature for followee recommendation on Twitter network
With the fast growth of the Twitter network, users are overwhelmed by the huge amount of information, which is shared via the follower/followee social network, to overcome this problem, finding like-minded users becomes a very important task. Thus, a system to assist users in such a task is recommended. In this paper, we propose a followee recommendation system by leveraging the topic feature, for topic modeling, and the follower/followee topology, searching for similar users to recommend, based on topic similarities. To show the effectiveness of our approach, we evaluate it using a dataset ingathered from the Twitter platform. The experiment results indicate that our model outperforms the lexical-based [reference?] approach and semantic-based approach [reference?], achieving a recall value of more than 23% on recommending 10 followees, proving that dealing with users’ topics of interest in microblogging websites content is more efficient than semantic and lexical features.
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