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On Utilizing Communities Detected From Social Networks in Hashtag Recommendation

机译：利用哈希特推荐中检测到社交网络中检测到的社区

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摘要

Personalized recommendation automatically predicts the top-y hashtags to a given tweet. Most research in the literature of hashtag recommendation focused on the content of the posts such as words and topics. Although these methods have measured the performance of hashtag recommendation on large data sets, there is a lack of analysis on how these methods perform on small communities. Motivated by the well-studied research area of community detection algorithms that aggregate strongly connected users with similar interests and behaviors, in this article, we propose a community-based hashtag recommendation framework, which studies hashtag recommendation through tweet similarity task and applies it on communities detected using the Clique percolation method, Louvain algorithm, and label propagation method. The detected communities are extracted from four social network constructions based on following, mention, hashtag, and topic. Compared to the three state-of-the-art hashtag recommendation methods, our extensive experiments show that our community-based method outperforms these methods, thus giving a higher hit rate. Our in-depth analysis demonstrates that the performance of hashtag recommendation is the best when the communities are generated using the Clique percolation method (CPM) from the network of users who share similar usage of hashtags.
机译：个性化推荐将自动预测给定推文的Top-Y HASHTAG。大多数研究的Hashtag建议的文献专注于帖子和主题等职位的内容。虽然这些方法测量了大数据集的HashTAG建议的性能，但缺乏对这些方法如何在小社区上执行的分析。由社区检测算法的良好研究领域的动机，这些研究领域将强大的联系用户具有相似的兴趣和行为，在本文中，我们提出了一个基于社区的HASHTAG推荐框架，这些建议框架通过推文相似任务研究Hashtag建议，并在社区应用使用Clique Percolation方法，Louvain算法和标签传播方法检测。基于以下内容，提及HashTag和主题，从四个社交网络结构中提取检测到的社区。与三个最先进的Hashtag推荐方法相比，我们广泛的实验表明，我们的社区的方法优于这些方法，从而产生更高的击中率。我们深入的分析表明，使用来自共享HashTags类似使用的Clique Percolation方法（CPM）生成社区时，HashTAG建议的性能是最佳的。

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