With the rapid growth of Twitter in recent years, there has been a tremendous increase in the number of tweets generated by users. Twitter allows users to make use of hashtags to facilitate effective categorization and retrieval of tweets. Despite the usefulness of hashtags, a major fraction of tweets do not contain hashtags. Several methods have been proposed to recommend hashtags based on lexical and topical features of tweets. However, semantic features and data sparsity in tweet representation have rarely been addressed by existing methods. In this paper, we propose a novel method for hashtag recommendation that resolves the data sparseness problem by exploiting the most relevant tweet information from external knowledge sources. In addition to lexical features and topical features, the proposed method incorporates the semantic features based on word-embeddings and user influence feature based on users’ influential position. To gain the advantage of various hashtag recommendation methods based on different features, our proposed method aggregates these methods using learning-to-rank and generates top-ranked hashtags. Experimental results show that the proposed method significantly outperforms the current state-of-the-art methods.

近年来，随着Twitter的快速发展，用户产生的推文数量已大大增加。Twitter允许用户使用主题标签来促进对tweet的有效分类和检索。尽管主题标签很有用，但大部分推文中都不包含主题标签。已经提出了几种基于推文的词汇和主题特征来推荐主题标签的方法。但是，现有方法很少解决tweet表示中的语义特征和数据稀疏性。在本文中，我们提出了一种新的主​​题标签推荐方法，该方法通过利用外部知识源中最相关的推文信息来解决数据稀疏问题。除了词汇功能和主题功能外，该方法结合了基于词嵌入的语义特征和基于用户影响位置的用户影响特征。为了获得基于不同功能的各种主题标签推荐方法的优势，我们提出的方法使用学习排名对这些方法进行汇总，并生成排名最高的主题标签。实验结果表明，所提出的方法明显优于当前的最新方法。